diff --git a/GraphHD_v2/basic/result1699333218.335392.csv b/GraphHD_v2/basic/result1699333218.335392.csv
new file mode 100644
index 00000000..f6f1c6a5
--- /dev/null
+++ b/GraphHD_v2/basic/result1699333218.335392.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.39,0.16,53.33,53.33,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.23,0.12,62.86,62.86,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.52,0.26,63.81,63.81,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.29,0.15,54.29,54.29,FHRR
diff --git a/GraphHD_v2/basic/result1699333236.2733052.csv b/GraphHD_v2/basic/result1699333236.2733052.csv
new file mode 100644
index 00000000..836aa40c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699333236.2733052.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.36,0.17,64.76,64.76,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.21,0.12,63.81,63.81,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.51,0.23,63.81,63.81,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.3,0.14,63.81,63.81,FHRR
diff --git a/GraphHD_v2/basic/result1699399798.518559.csv b/GraphHD_v2/basic/result1699399798.518559.csv
new file mode 100644
index 00000000..ec36a20a
--- /dev/null
+++ b/GraphHD_v2/basic/result1699399798.518559.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.51,0.2,59.05,59.05,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.24,0.11,36.84,36.84,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,7.05,3.48,50.53,50.53,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.06,0.59,12.22,12.22,BSC
diff --git a/GraphHD_v2/basic/result1699399947.0550241.csv b/GraphHD_v2/basic/result1699399947.0550241.csv
new file mode 100644
index 00000000..db8ac6f3
--- /dev/null
+++ b/GraphHD_v2/basic/result1699399947.0550241.csv
@@ -0,0 +1,4 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.44,0.21,56.19,56.19,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.24,0.15,33.33,33.33,BSC
diff --git a/GraphHD_v2/basic/result1699399982.062447.csv b/GraphHD_v2/basic/result1699399982.062447.csv
new file mode 100644
index 00000000..50594f70
--- /dev/null
+++ b/GraphHD_v2/basic/result1699399982.062447.csv
@@ -0,0 +1,4 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.45,0.2,43.81,43.81,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.25,0.13,31.58,31.58,BSC
diff --git a/GraphHD_v2/basic/result1699400007.153137.csv b/GraphHD_v2/basic/result1699400007.153137.csv
new file mode 100644
index 00000000..9ab57913
--- /dev/null
+++ b/GraphHD_v2/basic/result1699400007.153137.csv
@@ -0,0 +1,48 @@
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+PTC_FM,10000,0.43,0.19,37.14,37.14,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.23,0.12,33.33,33.33,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,8.28,3.28,50.45,50.45,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.15,0.52,17.78,17.78,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,2.61,1.05,59.58,59.58,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,16.52,7.5,54.24,54.24,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.29,0.17,55.24,55.24,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.18,0.09,82.46,82.46,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,4.58,2.22,65.45,65.45,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.75,0.35,36.67,36.67,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,1.57,0.72,70.36,70.36,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,10.86,4.55,71.47,71.47,MAP
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+PTC_FM,10000,0.99,0.41,53.33,53.33,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.63,0.29,82.46,82.46,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,19.84,8.55,60.91,60.91,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,4.0,1.64,36.11,36.11,HRR
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+PROTEINS,10000,8.07,4.23,68.56,68.56,HRR
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+DD,10000,77.69,28.39,76.55,76.55,HRR
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+PTC_FM,10000,0.35,0.22,55.24,55.24,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.23,0.12,85.96,85.96,FHRR
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+NCI1,10000,7.75,3.84,61.8,61.8,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.53,0.64,36.67,36.67,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,2.71,1.2,67.66,67.66,FHRR
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+DD,10000,22.84,8.59,72.88,72.88,FHRR
diff --git a/GraphHD_v2/basic/result1699478414.976448.csv b/GraphHD_v2/basic/result1699478414.976448.csv
new file mode 100644
index 00000000..820e686c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478414.976448.csv
@@ -0,0 +1,4 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.0,0.0,0.0,0.0,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.0,0.0,0.0,0.0,BSC
diff --git a/GraphHD_v2/basic/result1699478437.8803468.csv b/GraphHD_v2/basic/result1699478437.8803468.csv
new file mode 100644
index 00000000..89c61ed9
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478437.8803468.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.0,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699478446.3933382.csv b/GraphHD_v2/basic/result1699478446.3933382.csv
new file mode 100644
index 00000000..4724de82
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478446.3933382.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.01,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699478536.29761.csv b/GraphHD_v2/basic/result1699478536.29761.csv
new file mode 100644
index 00000000..41d8e6a1
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478536.29761.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.09,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699478578.291143.csv b/GraphHD_v2/basic/result1699478578.291143.csv
new file mode 100644
index 00000000..4bca11be
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478578.291143.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699478605.5848489.csv b/GraphHD_v2/basic/result1699478605.5848489.csv
new file mode 100644
index 00000000..4bca11be
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478605.5848489.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.08,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699478647.246716.csv b/GraphHD_v2/basic/result1699478647.246716.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478647.246716.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699478678.555866.csv b/GraphHD_v2/basic/result1699478678.555866.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478678.555866.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699478699.115463.csv b/GraphHD_v2/basic/result1699478699.115463.csv
new file mode 100644
index 00000000..4724de82
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478699.115463.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.01,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699478901.1126761.csv b/GraphHD_v2/basic/result1699478901.1126761.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699478901.1126761.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.03,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699479017.988084.csv b/GraphHD_v2/basic/result1699479017.988084.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699479017.988084.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.03,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699479038.843523.csv b/GraphHD_v2/basic/result1699479038.843523.csv
new file mode 100644
index 00000000..4724de82
--- /dev/null
+++ b/GraphHD_v2/basic/result1699479038.843523.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.01,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699479721.872013.csv b/GraphHD_v2/basic/result1699479721.872013.csv
new file mode 100644
index 00000000..3072e71f
--- /dev/null
+++ b/GraphHD_v2/basic/result1699479721.872013.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.06,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699481692.648533.csv b/GraphHD_v2/basic/result1699481692.648533.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699481692.648533.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699481739.722862.csv b/GraphHD_v2/basic/result1699481739.722862.csv
new file mode 100644
index 00000000..5d8d1e87
--- /dev/null
+++ b/GraphHD_v2/basic/result1699481739.722862.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.04,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699481968.7003891.csv b/GraphHD_v2/basic/result1699481968.7003891.csv
new file mode 100644
index 00000000..5037e684
--- /dev/null
+++ b/GraphHD_v2/basic/result1699481968.7003891.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699481978.6428401.csv b/GraphHD_v2/basic/result1699481978.6428401.csv
new file mode 100644
index 00000000..8b38d87b
--- /dev/null
+++ b/GraphHD_v2/basic/result1699481978.6428401.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699482001.435821.csv b/GraphHD_v2/basic/result1699482001.435821.csv
new file mode 100644
index 00000000..9f4bd7ac
--- /dev/null
+++ b/GraphHD_v2/basic/result1699482001.435821.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.05,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699482233.271428.csv b/GraphHD_v2/basic/result1699482233.271428.csv
new file mode 100644
index 00000000..5d8d1e87
--- /dev/null
+++ b/GraphHD_v2/basic/result1699482233.271428.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.04,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699483016.824408.csv b/GraphHD_v2/basic/result1699483016.824408.csv
new file mode 100644
index 00000000..3072e71f
--- /dev/null
+++ b/GraphHD_v2/basic/result1699483016.824408.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699483096.264558.csv b/GraphHD_v2/basic/result1699483096.264558.csv
new file mode 100644
index 00000000..26395aa2
--- /dev/null
+++ b/GraphHD_v2/basic/result1699483096.264558.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/basic/result1699483303.0820029.csv b/GraphHD_v2/basic/result1699483303.0820029.csv
new file mode 100644
index 00000000..6f677e14
--- /dev/null
+++ b/GraphHD_v2/basic/result1699483303.0820029.csv
@@ -0,0 +1,2 @@
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+PTC_FM,10000,0.14,0.0,0.0,0.0,MAP
diff --git a/GraphHD_v2/basic/result1699484311.550752.csv b/GraphHD_v2/basic/result1699484311.550752.csv
new file mode 100644
index 00000000..36700fc7
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484311.550752.csv
@@ -0,0 +1,10 @@
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+PTC_FM,10000,0.35,0.17,62.86,62.86,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.35,0.17,62.86,62.86,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.35,0.17,62.86,62.86,FHRR,eigen_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.35,0.17,62.86,62.86,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.35,0.17,62.86,62.86,FHRR,closeness_centrality
diff --git a/GraphHD_v2/basic/result1699484444.565254.csv b/GraphHD_v2/basic/result1699484444.565254.csv
new file mode 100644
index 00000000..3ad6944c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484444.565254.csv
@@ -0,0 +1,10 @@
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+PTC_FM,10000,0.35,0.22,60.95,60.95,FHRR,degree_centrality
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+PTC_FM,10000,0.35,0.22,60.95,60.95,FHRR,eigen_centrality
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+PTC_FM,10000,0.35,0.22,60.95,60.95,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
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diff --git a/GraphHD_v2/basic/result1699484455.0859709.csv b/GraphHD_v2/basic/result1699484455.0859709.csv
new file mode 100644
index 00000000..88c2d45c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484455.0859709.csv
@@ -0,0 +1,32 @@
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+PTC_FM,10000,0.34,0.17,57.14,57.14,FHRR,communicability_betweeness_centrality
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+PTC_FM,10000,0.34,0.17,57.14,57.14,FHRR,subgraph_centrality
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diff --git a/GraphHD_v2/basic/result1699484542.139751.csv b/GraphHD_v2/basic/result1699484542.139751.csv
new file mode 100644
index 00000000..76e9385b
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484542.139751.csv
@@ -0,0 +1,32 @@
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diff --git a/GraphHD_v2/basic/result1699484578.5374851.csv b/GraphHD_v2/basic/result1699484578.5374851.csv
new file mode 100644
index 00000000..b6f81a26
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484578.5374851.csv
@@ -0,0 +1,32 @@
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diff --git a/GraphHD_v2/basic/result1699484608.5689301.csv b/GraphHD_v2/basic/result1699484608.5689301.csv
new file mode 100644
index 00000000..19bda0b9
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484608.5689301.csv
@@ -0,0 +1,68 @@
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diff --git a/GraphHD_v2/basic/result1699484665.915395.csv b/GraphHD_v2/basic/result1699484665.915395.csv
new file mode 100644
index 00000000..8964fd13
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484665.915395.csv
@@ -0,0 +1,68 @@
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diff --git a/GraphHD_v2/basic/result1699484777.505989.csv b/GraphHD_v2/basic/result1699484777.505989.csv
new file mode 100644
index 00000000..3fddcaa6
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484777.505989.csv
@@ -0,0 +1,64 @@
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+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,communicability_betweeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,second_order_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.09,87.72,87.72,FHRR,second_order_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.97,2.55,61.31,61.31,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.97,2.55,61.31,61.31,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.97,2.55,61.31,61.31,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.97,2.55,61.31,61.31,FHRR,closeness_centrality
diff --git a/GraphHD_v2/basic/result1699484873.465694.csv b/GraphHD_v2/basic/result1699484873.465694.csv
new file mode 100644
index 00000000..2202f5ea
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484873.465694.csv
@@ -0,0 +1,10 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.02,2.41,60.91,60.91,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.02,2.41,60.91,60.91,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.02,2.41,60.91,60.91,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.02,2.41,60.91,60.91,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.02,2.41,60.91,60.91,FHRR,betweenness_centrality
diff --git a/GraphHD_v2/basic/result1699484922.6135309.csv b/GraphHD_v2/basic/result1699484922.6135309.csv
new file mode 100644
index 00000000..b6f3e548
--- /dev/null
+++ b/GraphHD_v2/basic/result1699484922.6135309.csv
@@ -0,0 +1,20 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,communicability_betweeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.96,2.56,63.58,63.58,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699485213.081866.csv b/GraphHD_v2/basic/result1699485213.081866.csv
new file mode 100644
index 00000000..582521be
--- /dev/null
+++ b/GraphHD_v2/basic/result1699485213.081866.csv
@@ -0,0 +1,30 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,communicability_betweeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.94,0.45,25.56,25.56,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.98,0.89,70.66,70.66,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.98,0.89,70.66,70.66,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.98,0.89,70.66,70.66,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.98,0.89,70.66,70.66,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.98,0.89,70.66,70.66,FHRR,betweenness_centrality
diff --git a/GraphHD_v2/basic/result1699485481.363292.csv b/GraphHD_v2/basic/result1699485481.363292.csv
new file mode 100644
index 00000000..a7c6a078
--- /dev/null
+++ b/GraphHD_v2/basic/result1699485481.363292.csv
@@ -0,0 +1,40 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.97,0.42,23.33,23.33,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,katz_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.97,0.94,70.06,70.06,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.42,7.57,73.73,73.73,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.42,7.57,73.73,73.73,FHRR,degree_centrality
diff --git a/GraphHD_v2/basic/result1699485717.082081.csv b/GraphHD_v2/basic/result1699485717.082081.csv
new file mode 100644
index 00000000..150e6311
--- /dev/null
+++ b/GraphHD_v2/basic/result1699485717.082081.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.83,6.09,74.01,74.01,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.83,6.09,74.01,74.01,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.83,6.09,74.01,74.01,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,15.83,6.09,74.01,74.01,FHRR,betweenness_centrality
diff --git a/GraphHD_v2/basic/result1699486143.9071412.csv b/GraphHD_v2/basic/result1699486143.9071412.csv
new file mode 100644
index 00000000..33356e68
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486143.9071412.csv
@@ -0,0 +1,18 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.75,0.4,42.22,42.22,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699486177.775477.csv b/GraphHD_v2/basic/result1699486177.775477.csv
new file mode 100644
index 00000000..55ccd58b
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486177.775477.csv
@@ -0,0 +1,18 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,38.33,38.33,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699486310.766479.csv b/GraphHD_v2/basic/result1699486310.766479.csv
new file mode 100644
index 00000000..84a3ee20
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486310.766479.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,betweenness_centrality
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+ENZYMES,10000,0.87,0.38,34.44,34.44,FHRR,load_centrality
diff --git a/GraphHD_v2/basic/result1699486591.8672829.csv b/GraphHD_v2/basic/result1699486591.8672829.csv
new file mode 100644
index 00000000..76668170
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486591.8672829.csv
@@ -0,0 +1,18 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,none
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+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,page_rank
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+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,degree_centrality
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+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,closeness_centrality
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+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,betweenness_centrality
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+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.84,0.36,22.22,22.22,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699486660.236307.csv b/GraphHD_v2/basic/result1699486660.236307.csv
new file mode 100644
index 00000000..4c2fd1c5
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486660.236307.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.96,0.45,25.56,25.56,FHRR
diff --git a/GraphHD_v2/basic/result1699486663.246925.csv b/GraphHD_v2/basic/result1699486663.246925.csv
new file mode 100644
index 00000000..b30e576c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486663.246925.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.01,0.43,20.0,20.0,FHRR
diff --git a/GraphHD_v2/basic/result1699486788.859935.csv b/GraphHD_v2/basic/result1699486788.859935.csv
new file mode 100644
index 00000000..ed785b3c
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486788.859935.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.03,0.47,32.78,32.78,FHRR
diff --git a/GraphHD_v2/basic/result1699486844.406713.csv b/GraphHD_v2/basic/result1699486844.406713.csv
new file mode 100644
index 00000000..4c994339
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486844.406713.csv
@@ -0,0 +1,18 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.9,0.37,23.33,23.33,FHRR,none
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+ENZYMES,10000,0.9,0.45,26.67,26.67,FHRR,page_rank
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+ENZYMES,10000,0.8,0.46,22.78,22.78,FHRR,degree_centrality
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+ENZYMES,10000,1.76,0.54,27.78,27.78,FHRR,closeness_centrality
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+ENZYMES,10000,1.36,0.67,16.11,16.11,FHRR,betweenness_centrality
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+ENZYMES,10000,1.33,0.68,21.11,21.11,FHRR,load_centrality
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+ENZYMES,10000,4.33,1.87,23.33,23.33,FHRR,subgraph_centrality
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+ENZYMES,10000,5.92,3.68,22.22,22.22,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.19,0.5,20.0,20.0,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699486890.615197.csv b/GraphHD_v2/basic/result1699486890.615197.csv
new file mode 100644
index 00000000..03ba4acb
--- /dev/null
+++ b/GraphHD_v2/basic/result1699486890.615197.csv
@@ -0,0 +1,18 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.85,0.41,23.22,23.22,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.99,0.49,21.52,21.52,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.02,0.48,21.7,21.7,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.12,0.52,23.93,23.93,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.49,0.76,21.67,21.67,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.36,0.63,22.11,22.11,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,3.88,1.89,22.19,22.19,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,4.91,2.13,21.67,21.67,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.11,0.55,22.41,22.41,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/basic/result1699762409.840183.csv b/GraphHD_v2/basic/result1699762409.840183.csv
new file mode 100644
index 00000000..c9b1488f
--- /dev/null
+++ b/GraphHD_v2/basic/result1699762409.840183.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,1.9,0.84,99.83,99.83,FHRR
diff --git a/GraphHD_v2/basic/result1699762424.5391848.csv b/GraphHD_v2/basic/result1699762424.5391848.csv
new file mode 100644
index 00000000..40cb1ba5
--- /dev/null
+++ b/GraphHD_v2/basic/result1699762424.5391848.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,1.69,0.89,99.5,99.5,FHRR
diff --git a/GraphHD_v2/basic/result1699762580.685486.csv b/GraphHD_v2/basic/result1699762580.685486.csv
new file mode 100644
index 00000000..e79e2002
--- /dev/null
+++ b/GraphHD_v2/basic/result1699762580.685486.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.89,0.34,71.04,71.04,FHRR
diff --git a/GraphHD_v2/basic/result1699762615.255841.csv b/GraphHD_v2/basic/result1699762615.255841.csv
new file mode 100644
index 00000000..672476e3
--- /dev/null
+++ b/GraphHD_v2/basic/result1699762615.255841.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.58,0.27,74.51,74.51,FHRR
diff --git a/GraphHD_v2/basic/result1699764795.516787.csv b/GraphHD_v2/basic/result1699764795.516787.csv
new file mode 100644
index 00000000..8bdecd03
--- /dev/null
+++ b/GraphHD_v2/basic/result1699764795.516787.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.7,0.27,72.62,72.62,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR_MD,10000,1.01,0.43,60.0,60.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,0.61,0.3,61.7,61.7,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2_MD,10000,1.37,0.62,53.41,53.41,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,1.02,0.49,67.4,67.4,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR_MD,10000,1.52,0.68,61.02,61.02,FHRR
diff --git a/GraphHD_v2/basic/result1699764944.035145.csv b/GraphHD_v2/basic/result1699764944.035145.csv
new file mode 100644
index 00000000..71ad7bd3
--- /dev/null
+++ b/GraphHD_v2/basic/result1699764944.035145.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.52,0.28,73.2,73.2,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR_MD,10000,0.98,0.45,61.96,61.96,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,0.64,0.31,62.73,62.73,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2_MD,10000,1.41,0.62,49.51,49.51,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,1.08,0.52,67.25,67.25,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR_MD,10000,1.55,0.71,59.24,59.24,FHRR
diff --git a/GraphHD_v2/basic/result1699911664.8755379.csv b/GraphHD_v2/basic/result1699911664.8755379.csv
new file mode 100644
index 00000000..5d82dfc2
--- /dev/null
+++ b/GraphHD_v2/basic/result1699911664.8755379.csv
@@ -0,0 +1,22 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,3.93,1.42,98.67,98.67,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.88,0.48,73.77,73.77,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR_MD,10000,1.43,0.54,66.3,66.3,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,1.0,0.48,57.45,57.45,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2_MD,10000,1.9,0.77,60.44,60.44,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,1.66,0.66,72.69,72.69,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR_MD,10000,2.01,0.8,66.95,66.95,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ER_MD,10000,1.9,0.92,72.39,72.39,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+FRANKENSTEIN,10000,4.44,2.18,60.22,60.22,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MCF-7,10000,43.82,20.02,67.75,67.75,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MCF-7H,10000,59.18,27.56,66.79,66.79,FHRR
diff --git a/GraphHD_v2/basic_centrality/result1699477760.426856.csv b/GraphHD_v2/basic_centrality/result1699477760.426856.csv
new file mode 100644
index 00000000..3b8baed9
--- /dev/null
+++ b/GraphHD_v2/basic_centrality/result1699477760.426856.csv
@@ -0,0 +1,48 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.51,0.19,62.86,62.86,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.22,0.12,31.58,31.58,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,6.49,3.03,48.18,48.18,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.24,0.72,18.33,18.33,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,2.5,1.29,58.68,58.68,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,18.0,7.28,57.34,57.34,BSC
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.31,0.14,66.67,66.67,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.16,0.08,78.95,78.95,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,4.96,2.41,64.72,64.72,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.85,0.35,21.67,21.67,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,1.43,0.7,70.96,70.96,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,10.59,4.54,75.42,75.42,MAP
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.86,0.41,59.05,59.05,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.59,0.25,77.19,77.19,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,20.48,8.64,65.29,65.29,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,4.52,2.01,21.11,21.11,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,8.38,3.77,67.66,67.66,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,72.19,28.99,66.67,66.67,HRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.39,0.19,60.0,60.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.23,0.12,87.72,87.72,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,6.95,3.3,63.18,63.18,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,1.16,0.64,20.56,20.56,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PROTEINS,10000,3.26,1.11,67.07,67.07,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DD,10000,20.3,8.46,73.73,73.73,FHRR
diff --git a/GraphHD_v2/basic_node_attr/result1699911901.148511.csv b/GraphHD_v2/basic_node_attr/result1699911901.148511.csv
new file mode 100644
index 00000000..84c1f421
--- /dev/null
+++ b/GraphHD_v2/basic_node_attr/result1699911901.148511.csv
@@ -0,0 +1,78 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,0.03,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR_MD,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2_MD,10000,0.02,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR_MD,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ER_MD,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+FRANKENSTEIN,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MCF-7,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MCF-7H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MOLT-4,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MOLT-4H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+Mutagenicity,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MUTAG,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI1,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI109,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI-H23,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+NCI-H23H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+OVCAR-8,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+OVCAR-8H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+P388,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+P388H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PC-3,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PC-3H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FM,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_FR,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_MM,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+PTC_MR,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SF-295,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SF-295H,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SN12C,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SN12CH,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SW-620,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+SW-620H,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+UACC257,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+UACC257H,10000,0.01,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+Yeast,10000,0.0,0.0,0.0,0.0,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+YeastH,10000,0.01,0.0,0.0,0.0,FHRR
diff --git a/GraphHD_v2/basic_node_attr/result1699912072.739661.csv b/GraphHD_v2/basic_node_attr/result1699912072.739661.csv
new file mode 100644
index 00000000..b6c96a9c
--- /dev/null
+++ b/GraphHD_v2/basic_node_attr/result1699912072.739661.csv
@@ -0,0 +1,40 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,0.02,1.2,78.17,78.17,FHRR
+BZR,10000,0.0,0.37,33.61,33.61,FHRR
+BZR_MD,10000,0.02,0.77,44.57,44.57,FHRR
+COX2,10000,0.01,1.19,67.38,67.38,FHRR
+COX2_MD,10000,0.02,1.82,48.35,48.35,FHRR
+DHFR,10000,0.0,0.83,54.19,54.19,FHRR
+DHFR_MD,10000,0.02,0.98,71.19,71.19,FHRR
+ER_MD,10000,0.01,0.85,55.22,55.22,FHRR
+FRANKENSTEIN,10000,0.0,2.09,55.76,55.76,FHRR
+MCF-7,10000,0.0,20.64,65.06,65.06,FHRR
+MCF-7H,10000,0.01,33.06,66.75,66.75,FHRR
+MOLT-4,10000,0.0,32.0,66.42,66.42,FHRR
+MOLT-4H,10000,0.01,45.56,73.78,73.78,FHRR
+Mutagenicity,10000,0.0,3.34,54.69,54.69,FHRR
+MUTAG,10000,0.0,0.12,42.11,42.11,FHRR
+NCI1,10000,0.0,3.18,50.04,50.04,FHRR
+NCI109,10000,0.0,3.19,53.19,53.19,FHRR
+NCI-H23,10000,0.01,28.07,74.57,74.57,FHRR
+NCI-H23H,10000,0.0,48.16,64.5,64.5,FHRR
+OVCAR-8,10000,0.0,34.09,82.81,82.81,FHRR
+OVCAR-8H,10000,0.0,48.88,73.38,73.38,FHRR
+P388,10000,0.0,32.07,58.37,58.37,FHRR
+P388H,10000,0.01,50.94,70.56,70.56,FHRR
+PC-3,10000,0.0,23.33,70.59,70.59,FHRR
+PC-3H,10000,0.0,32.17,78.23,78.23,FHRR
+PTC_FM,10000,0.0,0.19,48.57,48.57,FHRR
+PTC_FR,10000,0.0,0.19,39.62,39.62,FHRR
+PTC_MM,10000,0.0,0.18,56.44,56.44,FHRR
+PTC_MR,10000,0.0,0.18,51.92,51.92,FHRR
+SF-295,10000,0.01,29.5,81.92,81.92,FHRR
+SF-295H,10000,0.01,46.45,71.82,71.82,FHRR
+SN12C,10000,0.01,32.21,77.37,77.37,FHRR
+SN12CH,10000,0.01,52.6,31.39,31.39,FHRR
+SW-620,10000,0.0,30.24,74.19,74.19,FHRR
+SW-620H,10000,0.0,43.84,54.67,54.67,FHRR
+UACC257,10000,0.01,27.66,75.26,75.26,FHRR
+UACC257H,10000,0.0,39.69,79.5,79.5,FHRR
+Yeast,10000,0.01,52.67,67.16,67.16,FHRR
+YeastH,10000,0.0,68.76,70.09,70.09,FHRR
diff --git a/GraphHD_v2/experiment_1/result1699228665.124537.csv b/GraphHD_v2/experiment_1/result1699228665.124537.csv
new file mode 100644
index 00000000..d7afb034
--- /dev/null
+++ b/GraphHD_v2/experiment_1/result1699228665.124537.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1
+PTC_FM,10000,0.24,0.11,58.1,58.1
+dataset,dimensions,train_time,test_time,accuracy,f1
+MUTAG,10000,0.14,0.07,77.19,77.19
+dataset,dimensions,train_time,test_time,accuracy,f1
+NCI1,10000,3.31,1.64,60.75,60.75
+dataset,dimensions,train_time,test_time,accuracy,f1
+ENZYMES,10000,0.52,0.27,19.44,19.44
+dataset,dimensions,train_time,test_time,accuracy,f1
+PROTEINS,10000,1.27,0.55,67.96,67.96
+dataset,dimensions,train_time,test_time,accuracy,f1
+DD,10000,9.24,3.39,74.58,74.58
diff --git a/GraphHD_v2/experiment_1/result1699228755.18482.csv b/GraphHD_v2/experiment_1/result1699228755.18482.csv
new file mode 100644
index 00000000..5ccad83c
--- /dev/null
+++ b/GraphHD_v2/experiment_1/result1699228755.18482.csv
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+++ b/GraphHD_v2/experiment_aux/result1699309075.3794801.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1
+PTC_FR,10000,0.24,0.13,38.68,38.68
+dataset,dimensions,train_time,test_time,accuracy,f1
+MUTAG,10000,0.14,0.07,85.96,85.96
+dataset,dimensions,train_time,test_time,accuracy,f1
+NCI1,10000,5.01,2.29,62.37,62.37
+dataset,dimensions,train_time,test_time,accuracy,f1
+ENZYMES,10000,1.15,0.5,40.0,40.0
+dataset,dimensions,train_time,test_time,accuracy,f1
+PROTEINS,10000,2.56,1.03,62.57,62.57
+dataset,dimensions,train_time,test_time,accuracy,f1
+DD,10000,33.49,12.56,68.36,68.36
diff --git a/GraphHD_v2/experiment_aux/result1699309352.056192.csv b/GraphHD_v2/experiment_aux/result1699309352.056192.csv
new file mode 100644
index 00000000..4cef58c6
--- /dev/null
+++ b/GraphHD_v2/experiment_aux/result1699309352.056192.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1
+PTC_FR,10000,0.78,0.36,54.72,54.72
+dataset,dimensions,train_time,test_time,accuracy,f1
+MUTAG,10000,0.44,0.21,70.18,70.18
+dataset,dimensions,train_time,test_time,accuracy,f1
+NCI1,10000,9.92,3.88,63.83,63.83
+dataset,dimensions,train_time,test_time,accuracy,f1
+ENZYMES,10000,1.78,0.75,35.0,35.0
+dataset,dimensions,train_time,test_time,accuracy,f1
+PROTEINS,10000,4.39,1.96,69.76,69.76
+dataset,dimensions,train_time,test_time,accuracy,f1
+DD,10000,40.12,16.63,50.85,50.85
diff --git a/GraphHD_v2/experiment_aux/result1699309550.196883.csv b/GraphHD_v2/experiment_aux/result1699309550.196883.csv
new file mode 100644
index 00000000..219f806e
--- /dev/null
+++ b/GraphHD_v2/experiment_aux/result1699309550.196883.csv
@@ -0,0 +1,12 @@
+dataset,dimensions,train_time,test_time,accuracy,f1
+PTC_FR,10000,0.93,0.38,48.11,48.11
+dataset,dimensions,train_time,test_time,accuracy,f1
+MUTAG,10000,0.4,0.2,75.44,75.44
+dataset,dimensions,train_time,test_time,accuracy,f1
+NCI1,10000,25.69,10.57,62.29,62.29
+dataset,dimensions,train_time,test_time,accuracy,f1
+ENZYMES,10000,1.61,0.63,35.0,35.0
+dataset,dimensions,train_time,test_time,accuracy,f1
+PROTEINS,10000,3.14,1.51,70.06,70.06
+dataset,dimensions,train_time,test_time,accuracy,f1
+DD,10000,310.85,77.11,74.58,74.58
diff --git a/GraphHD_v2/experiment_aux/result1699318066.963996.csv b/GraphHD_v2/experiment_aux/result1699318066.963996.csv
new file mode 100644
index 00000000..b0e34c42
--- /dev/null
+++ b/GraphHD_v2/experiment_aux/result1699318066.963996.csv
@@ -0,0 +1,6 @@
+dataset,dimensions,train_time,test_time,accuracy,f1
+PTC_FR,10000,1.76,1.11,53.77,53.77
+dataset,dimensions,train_time,test_time,accuracy,f1
+MUTAG,10000,0.97,0.43,75.44,75.44
+dataset,dimensions,train_time,test_time,accuracy,f1
+NCI1,10000,51.76,22.65,64.31,64.31
diff --git a/GraphHD_v2/experiments.txt b/GraphHD_v2/experiments.txt
new file mode 100644
index 00000000..f3e73a00
--- /dev/null
+++ b/GraphHD_v2/experiments.txt
@@ -0,0 +1 @@
+1. Experiment evaluating encoding
\ No newline at end of file
diff --git a/GraphHD_v2/graphhd.py b/GraphHD_v2/graphhd.py
new file mode 100644
index 00000000..cfc10a16
--- /dev/null
+++ b/GraphHD_v2/graphhd.py
@@ -0,0 +1,162 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+
+
+def experiment(randomness=0):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = 10000  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Level(size, out_features, randomness=0.05)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes)
+    model = model.to(device)
+
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+    auc = torchmetrics.AUROC("multiclass", num_classes=graphs.num_classes)
+
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+            auc.update(outputs.cpu(), samples.y)
+
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    au = auc.compute().item() * 100
+    print(f"Testing accuracy of {acc:.3f}%")
+    print(f"Testing f1 of {f:.3f}%")
+    print(f"Testing AUC of {au:.3f}%")
+    return acc, f, au
+
+
+REPETITIONS = 10
+RANDOMNESS = [0, 0.00001, 0.0001, 0.001, 0.01, 0.05, 0.1, 0.15, 0.2, 0.4, 0.6, 0.8, 1]
+
+
+acc_final = []
+f1_final = []
+auc_final = []
+
+for i in RANDOMNESS:
+    acc_aux = []
+    f1_aux = []
+    auc_aux = []
+    for j in range(REPETITIONS):
+        acc, f1, auc = experiment(i)
+        acc_aux.append(acc)
+        f1_aux.append(f1)
+        auc_aux.append(auc)
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+    auc_final.append(round(sum(auc_aux) / REPETITIONS, 2))
+
+print(acc_final)
+print(f1_final)
+print(auc_final)
diff --git a/GraphHD_v2/graphhd_basic.py b/GraphHD_v2/graphhd_basic.py
new file mode 100644
index 00000000..be1ad508
--- /dev/null
+++ b/GraphHD_v2/graphhd_basic.py
@@ -0,0 +1,258 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "basic/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            if embed == "thermometer":
+                self.node_ids = embeddings.Thermometer(size, out_features, vsa=VSA)
+            elif embed == "circular":
+                self.node_ids = embeddings.Circular(size, out_features, vsa=VSA)
+            elif embed == "projection":
+                self.node_ids = embeddings.Projection(size, out_features, vsa=VSA)
+            elif embed == "sinusoid":
+                self.node_ids = embeddings.Sinusoid(size, out_features, vsa=VSA)
+            elif embed == "density":
+                self.node_ids = embeddings.Density(size, out_features, vsa=VSA)
+            else:
+                self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 25
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_basic_centrality.py b/GraphHD_v2/graphhd_basic_centrality.py
new file mode 100644
index 00000000..ed5a4cb8
--- /dev/null
+++ b/GraphHD_v2/graphhd_basic_centrality.py
@@ -0,0 +1,219 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+import torch_geometric.utils
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "basic_centrality/result" + str(time.time()) + ".csv"
+DIM = 10000
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            if embed == "thermometer":
+                self.node_ids = embeddings.Thermometer(size, out_features, vsa=VSA)
+            elif embed == "circular":
+                self.node_ids = embeddings.Circular(size, out_features, vsa=VSA)
+            elif embed == "projection":
+                self.node_ids = embeddings.Projection(size, out_features, vsa=VSA)
+            elif embed == "sinusoid":
+                self.node_ids = embeddings.Sinusoid(size, out_features, vsa=VSA)
+            elif embed == "density":
+                self.node_ids = embeddings.Density(size, out_features, vsa=VSA)
+            else:
+                self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            nodes, _ = x.edge_index
+            indexs = list(map(int, torch_geometric.utils.degree(nodes)))
+
+            try:
+                node_id_hvs = torchhd.bind(node_id_hvs, self.levels.weight[indexs])
+                # node_id_hvs = torchhd.bind(node_id_hvs, self.node_attr2(x.x))
+            except Exception as e:
+                print("err " + str(e))
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 50
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FM", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+VSAS = ["BSC", "MAP", "HRR", "FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_basic_node_attr.py b/GraphHD_v2/graphhd_basic_node_attr.py
new file mode 100644
index 00000000..8d84c740
--- /dev/null
+++ b/GraphHD_v2/graphhd_basic_node_attr.py
@@ -0,0 +1,265 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "basic_node_attr/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            if len(x.x[0]) > 0:
+                indices_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+                )
+                node_id_hvs = torchhd.bind(
+                    node_id_hvs, self.node_attr.weight[indices_tensor]
+                )
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+            break
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]
+VSAS = ["FHRR"]
+
+with open(csv_file, mode="a", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(
+        [
+            "dataset",
+            "dimensions",
+            "train_time",
+            "test_time",
+            "accuracy",
+            "f1",
+            "VSA",
+        ]
+    )
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_experiment_1.py b/GraphHD_v2/graphhd_experiment_1.py
new file mode 100644
index 00000000..cb41a1a6
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_1.py
@@ -0,0 +1,187 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_1/result" + str(time.time()) + ".csv"
+DIM = 10000
+VSA = "FHRR"
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            if embed == "thermometer":
+                self.node_ids = embeddings.Thermometer(size, out_features, vsa=VSA)
+            elif embed == "circular":
+                self.node_ids = embeddings.Circular(size, out_features, vsa=VSA)
+            elif embed == "projection":
+                self.node_ids = embeddings.Projection(size, out_features, vsa=VSA)
+            elif embed == "sinusoid":
+                self.node_ids = embeddings.Sinusoid(size, out_features, vsa=VSA)
+            elif embed == "density":
+                self.node_ids = embeddings.Density(size, out_features, vsa=VSA)
+            else:
+                self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FM", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    train_final = []
+    test_final = []
+
+    for i in RANDOMNESS:
+        acc_aux = []
+        f1_aux = []
+        train_aux = []
+        test_aux = []
+        for j in range(REPETITIONS):
+            acc, f1, train_t, test_t = experiment(1, i, d)
+            acc_aux.append(acc)
+            f1_aux.append(f1)
+            train_aux.append(train_t)
+            test_aux.append(test_t)
+        acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+        f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+        train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+        test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow(
+            ["dataset", "dimensions", "train_time", "test_time", "accuracy", "f1"]
+        )
+        writer.writerows(
+            [[d, DIM, train_final[0], test_final[0], acc_final[0], f1_final[0]]]
+        )
diff --git a/GraphHD_v2/graphhd_experiment_2.py b/GraphHD_v2/graphhd_experiment_2.py
new file mode 100644
index 00000000..359ad72d
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_2.py
@@ -0,0 +1,189 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_2/result" + str(time.time()) + ".csv"
+DIM = 10000
+VSA = "FHRR"
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            if embed == "thermometer":
+                self.node_ids = embeddings.Thermometer(size, out_features, vsa=VSA)
+            elif embed == "circular":
+                self.node_ids = embeddings.Circular(size, out_features, vsa=VSA)
+            elif embed == "projection":
+                self.node_ids = embeddings.Projection(size, out_features, vsa=VSA)
+            elif embed == "sinusoid":
+                self.node_ids = embeddings.Sinusoid(size, out_features, vsa=VSA)
+            elif embed == "density":
+                self.node_ids = embeddings.Density(size, out_features, vsa=VSA)
+            else:
+                self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FR", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    train_final = []
+    test_final = []
+
+    for i in RANDOMNESS:
+        acc_aux = []
+        f1_aux = []
+        train_aux = []
+        test_aux = []
+        for j in range(REPETITIONS):
+            acc, f1, train_t, test_t = experiment(1, i, d)
+            acc_aux.append(acc)
+            f1_aux.append(f1)
+            train_aux.append(train_t)
+            test_aux.append(test_t)
+        acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+        f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+        train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+        test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow(
+            ["dataset", "dimensions", "train_time", "test_time", "accuracy", "f1"]
+        )
+        writer.writerows(
+            [[d, DIM, train_final[0], test_final[0], acc_final[0], f1_final[0]]]
+        )
diff --git a/GraphHD_v2/graphhd_experiment_3.py b/GraphHD_v2/graphhd_experiment_3.py
new file mode 100644
index 00000000..e9b64bb0
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_3.py
@@ -0,0 +1,218 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch_geometric.utils
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_3/result" + str(time.time()) + ".csv"
+DIM = 10000
+VSA = "HRR"
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def local_centrality2(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def local_centrality(self, x):
+            nodes, _ = x.edge_index
+            indexs = list(map(int, torch_geometric.utils.degree(nodes)))
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            try:
+                node_id_hvs = torchhd.bind(
+                    self.node_ids.weight[list(range(x.num_nodes))],
+                    self.levels.weight[indexs],
+                )
+            except:
+                print("err")
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def semi_local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
+                node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bundle(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def forward(self, x):
+            return self.local_centrality(x)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FR", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    train_final = []
+    test_final = []
+
+    for i in RANDOMNESS:
+        acc_aux = []
+        f1_aux = []
+        train_aux = []
+        test_aux = []
+        for j in range(REPETITIONS):
+            acc, f1, train_t, test_t = experiment(1, i, d)
+            acc_aux.append(acc)
+            f1_aux.append(f1)
+            train_aux.append(train_t)
+            test_aux.append(test_t)
+        acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+        f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+        train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+        test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow(
+            ["dataset", "dimensions", "train_time", "test_time", "accuracy", "f1"]
+        )
+        writer.writerows(
+            [[d, DIM, train_final[0], test_final[0], acc_final[0], f1_final[0]]]
+        )
diff --git a/GraphHD_v2/graphhd_experiment_33.py b/GraphHD_v2/graphhd_experiment_33.py
new file mode 100644
index 00000000..1d0ca17a
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_33.py
@@ -0,0 +1,170 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_3/result" + str(time.time()) + ".csv"
+
+
+def experiment(randomness=0, dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = 10000  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features)
+
+        def forward(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            node_id_hvs_2 = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs_2[i] = node_id_hvs[i]
+                for j in adjacent_nodes:
+                    node_id_hvs_2[i] += torchhd.permute(node_id_hvs[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes)
+    model = model.to(device)
+
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f
+
+
+REPETITIONS = 5
+DATASET = ["MUTAG", "ENZYMES", "PROTEINS"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    acc_aux = []
+    f1_aux = []
+    for j in range(REPETITIONS):
+        acc, f1 = experiment(100, d)
+        acc_aux.append(acc)
+        f1_aux.append(f1)
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow([d] + ["RANDOM"])
+        writer.writerows([acc_final])
+        writer.writerows([f1_final])
diff --git a/GraphHD_v2/graphhd_experiment_4.py b/GraphHD_v2/graphhd_experiment_4.py
new file mode 100644
index 00000000..569c5303
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_4.py
@@ -0,0 +1,219 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_4/result" + str(time.time()) + ".csv"
+
+
+def experiment(randomness=0, dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = 10000  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features)
+            self.levels = embeddings.Circular(size, out_features)
+
+        def local_centrality2(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = torchhd.bind(
+                    self.node_ids.weight[i], self.levels.weight[len(adjacent_nodes)]
+                )
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def semi_local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
+                node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bundle(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def forward(self, x):
+            return self.local_centrality(x)
+            """
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            node_id_hvs_2 = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs_2[i] = node_id_hvs[i]
+                for j in adjacent_nodes:
+                    node_id_hvs_2[i] += torchhd.permute(node_id_hvs[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
+            return torchhd.multiset(hvs)
+            """
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes)
+    model = model.to(device)
+
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f
+
+
+REPETITIONS = 1
+DATASET = ["MUTAG"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    acc_aux = []
+    f1_aux = []
+    for j in range(REPETITIONS):
+        acc, f1 = experiment(100, d)
+        acc_aux.append(acc)
+        f1_aux.append(f1)
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow([d] + ["RANDOM"])
+        writer.writerows([acc_final])
+        writer.writerows([f1_final])
diff --git a/GraphHD_v2/graphhd_experiment_5.py b/GraphHD_v2/graphhd_experiment_5.py
new file mode 100644
index 00000000..cf8dcf1e
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_5.py
@@ -0,0 +1,208 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_4/result" + str(time.time()) + ".csv"
+
+
+def experiment(randomness=0, dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = 10000  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, node_features):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+
+            self.node_ids = embeddings.Random(size, out_features)
+            self.node_attr = embeddings.Density(node_features, out_features)
+
+            self.levels = embeddings.Circular(size, out_features)
+
+        def local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            # for i in nodes:
+            #    node_id_hvs[i] = torchhd.bind(self.node_ids.weight[i], self.node_attr(x.x[i]))
+
+            node_id_hvs = torchhd.bind(self.node_ids.weight, self.node_attr(x.x))
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+
+            return torchhd.multiset(hvs)
+
+        def semi_local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
+                node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bundle(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def forward(self, x):
+            return self.local_centrality(x)
+            """
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            node_id_hvs_2 = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs_2[i] = node_id_hvs[i]
+                for j in adjacent_nodes:
+                    node_id_hvs_2[i] += torchhd.permute(node_id_hvs[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
+            return torchhd.multiset(hvs)
+            """
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, len(graphs.x[0]))
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes)
+    model = model.to(device)
+
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f
+
+
+REPETITIONS = 1
+DATASET = ["MUTAG", "ENZYMES", "PROTEINS"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    acc_aux = []
+    f1_aux = []
+    for j in range(REPETITIONS):
+        acc, f1 = experiment(100, d)
+        acc_aux.append(acc)
+        f1_aux.append(f1)
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow([d] + ["RANDOM"])
+        writer.writerows([acc_final])
+        writer.writerows([f1_final])
diff --git a/GraphHD_v2/graphhd_experiment_aux.py b/GraphHD_v2/graphhd_experiment_aux.py
new file mode 100644
index 00000000..1c5ec547
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_aux.py
@@ -0,0 +1,222 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch_geometric.utils
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "experiment_aux/result" + str(time.time()) + ".csv"
+DIM = 10000
+VSA = "FHRR"
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def to_undirected_attr(edge_index, edge_attr):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+
+        unique_elements, inverse_indices = torch.unique(
+            edge_index, dim=1, return_inverse=True
+        )
+
+        unique_lists = [inverse_indices == i for i in range(len(unique_elements.t()))]
+        first_indices = [
+            indices.nonzero(as_tuple=False)[0, 0].item() for indices in unique_lists
+        ]
+
+        if edge_attr is not None:
+            attr_edge = edge_attr[first_indices]
+        else:
+            attr_edge = None
+
+        return edge_index[0][first_indices], edge_index[1][first_indices], attr_edge
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, edge_features, node_features):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.edge_attr = embeddings.Random(edge_features, out_features, vsa=VSA)
+            self.edge_attr2 = embeddings.Density(edge_features, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(node_features, out_features, vsa=VSA)
+            self.node_attr2 = embeddings.Density(node_features, out_features, vsa=VSA)
+
+        def local_centrality(self, x):
+            nodes, _ = x.edge_index
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            indexs = list(map(int, torch_geometric.utils.degree(nodes)))
+
+            row, col, edge_attr = to_undirected_attr(x.edge_index, x.edge_attr)
+
+            try:
+                node_id_hvs = torchhd.bind(
+                    self.node_ids.weight[list(range(x.num_nodes))],
+                    self.levels.weight[indexs],
+                )
+                # node_id_hvs = torchhd.bind(node_id_hvs, self.node_attr.weight[x.x.argmax().item()])
+                node_id_hvs = torchhd.bind(node_id_hvs, self.node_attr2(x.x))
+            except:
+                print("err")
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            if edge_attr is not None:
+                # hvs = torchhd.bind(hvs, self.edge_attr.weight[edge_attr.argmax().item()])
+                hvs = torchhd.bind(hvs, self.edge_attr2(edge_attr))
+            return torchhd.multiset(hvs)
+
+        def forward(self, x):
+            return self.local_centrality(x)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+
+    encode = Encoder(
+        DIMENSIONS, max_graph_size, graphs.num_edge_labels, graphs.num_node_labels
+    )
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FR", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+
+for d in DATASET:
+    acc_final = []
+    f1_final = []
+    train_final = []
+    test_final = []
+
+    for i in RANDOMNESS:
+        acc_aux = []
+        f1_aux = []
+        train_aux = []
+        test_aux = []
+        for j in range(REPETITIONS):
+            acc, f1, train_t, test_t = experiment(1, i, d)
+            acc_aux.append(acc)
+            f1_aux.append(f1)
+            train_aux.append(train_t)
+            test_aux.append(test_t)
+        acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+        f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+        train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+        test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+    with open(csv_file, mode="a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow(
+            ["dataset", "dimensions", "train_time", "test_time", "accuracy", "f1"]
+        )
+        writer.writerows(
+            [[d, DIM, train_final[0], test_final[0], acc_final[0], f1_final[0]]]
+        )
diff --git a/GraphHD_v2/graphhd_experiment_encoding_centrality.py b/GraphHD_v2/graphhd_experiment_encoding_centrality.py
new file mode 100644
index 00000000..381e5ce8
--- /dev/null
+++ b/GraphHD_v2/graphhd_experiment_encoding_centrality.py
@@ -0,0 +1,239 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch_geometric.utils
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "encoding_centrality/result" + str(time.time()) + ".csv"
+DIM = 10000
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def local_centrality2(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                node_id_hvs[i] = self.node_ids.weight[i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] += torchhd.permute(self.node_ids.weight[j])
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def local_centrality(self, x):
+            nodes, _ = x.edge_index
+            indexs = list(map(int, torch_geometric.utils.degree(nodes)))
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            try:
+                node_id_hvs = torchhd.bind(
+                    self.node_ids.weight[list(range(x.num_nodes))],
+                    self.levels.weight[indexs],
+                )
+            except:
+                print("err")
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def semi_local_centrality(self, x):
+            nodes, _ = x.edge_index
+            nodes = list(set(nodes))
+            node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
+
+            for i in nodes:
+                adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
+                for j in adjacent_nodes:
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
+                node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
+
+            row, col = to_undirected(x.edge_index)
+            hvs = torchhd.bundle(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+        def forward(self, x):
+            return self.local_centrality(x)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 100
+RANDOMNESS = ["random"]
+DATASET = ["PTC_FM", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+VSAS = ["BSC", "MAP", "HRR", "FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_level.py b/GraphHD_v2/graphhd_level.py
new file mode 100644
index 00000000..b987c4b6
--- /dev/null
+++ b/GraphHD_v2/graphhd_level.py
@@ -0,0 +1,296 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "level/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            # node_id_hvs = self.node_ids.weight[: x.num_nodes]
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            indices_tensor = torch.stack(
+                [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+            )
+            node_attr = self.node_attr.weight[indices_tensor]
+            node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            prev = row[0]
+
+            final_hv = torchhd.empty(1, self.out_features, VSA)
+            aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            for idx in range(len(x.edge_index[0])):
+                i = x.edge_index[0][idx]
+                j = x.edge_index[1][idx]
+                if prev == i:
+                    aux_hv = torchhd.bind(
+                        aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                    )
+                else:
+                    prev = i
+                    final_hv = torchhd.bundle(final_hv, aux_hv)
+                    aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            return final_hv[0]
+
+        def forward_hashmap_label_random(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            indices_tensor = torch.stack(
+                [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+            )
+            node_attr = self.node_attr.weight[indices_tensor]
+            node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            prev = row[0]
+
+            final_hv = torchhd.empty(1, self.out_features, VSA)
+            aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            for idx in range(len(x.edge_index[0])):
+                i = x.edge_index[0][idx]
+                j = x.edge_index[1][idx]
+                if prev == i:
+                    aux_hv = torchhd.bind(
+                        aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                    )
+                else:
+                    prev = i
+                    final_hv = torchhd.bundle(final_hv, aux_hv)
+                    aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            return final_hv[0]
+
+        def forward1(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+            prev = row[0]
+
+            final_hv = torchhd.empty(1, self.out_features, VSA)
+            aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            for idx in range(len(x.edge_index[0])):
+                i = x.edge_index[0][idx]
+                j = x.edge_index[1][idx]
+                if prev == i:
+                    aux_hv = torchhd.bind(
+                        aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                    )
+                else:
+                    prev = i
+                    final_hv = torchhd.bundle(final_hv, aux_hv)
+                    aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            return final_hv[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 20
+RANDOMNESS = ["random"]
+DATASET = ["BZR", "BZR_MD", "COX2", "COX2_MD", "DHFR", "DHFR_MD"]
+# ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_basic.py b/GraphHD_v2/graphhd_list_basic.py
new file mode 100644
index 00000000..265b64c4
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_basic.py
@@ -0,0 +1,272 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_basic/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]
+
+
+# ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+with open(csv_file, mode="a", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(
+        [
+            "dataset",
+            "dimensions",
+            "train_time",
+            "test_time",
+            "accuracy",
+            "f1",
+            "VSA",
+        ]
+    )
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_basic_node_attr.py b/GraphHD_v2/graphhd_list_basic_node_attr.py
new file mode 100644
index 00000000..d577170f
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_basic_node_attr.py
@@ -0,0 +1,285 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_basic_node/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            if len(x.x[0]) > 0:
+                indices_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+                )
+                node_attr = self.node_attr.weight[indices_tensor]
+                node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]
+
+# ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+with open(csv_file, mode="a", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(
+        [
+            "dataset",
+            "dimensions",
+            "train_time",
+            "test_time",
+            "accuracy",
+            "f1",
+            "VSA",
+        ]
+    )
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_basic_node_attr2.py b/GraphHD_v2/graphhd_list_basic_node_attr2.py
new file mode 100644
index 00000000..f8298d08
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_basic_node_attr2.py
@@ -0,0 +1,275 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_basic_node2/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            if len(x.x[0]) > 0:
+                indices_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+                )
+                node_attr = self.node_attr.weight[indices_tensor]
+                node_id_hvs = torchhd.bind(node_id_hvs, torchhd.permute(node_attr))
+
+            row, col = to_undirected(x.edge_index)
+
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = [
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]
+
+# ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+with open(csv_file, mode="a", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(
+        [
+            "dataset",
+            "dimensions",
+            "train_time",
+            "test_time",
+            "accuracy",
+            "f1",
+            "VSA",
+        ]
+    )
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_basic_node_attr_edge.py b/GraphHD_v2/graphhd_list_basic_node_attr_edge.py
new file mode 100644
index 00000000..ee5d2435
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_basic_node_attr_edge.py
@@ -0,0 +1,258 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "node_attr/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+            self.edge_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            if len(x.x[0]) > 0:
+                indices_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+                )
+                node_attr = self.node_attr.weight[indices_tensor]
+                node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            has_attr = x.edge_attr != None
+            if has_attr:
+                edges_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.edge_attr.unbind()]
+                )
+                edge_attr = self.edge_attr.weight[edges_tensor]
+
+            row, col = to_undirected(x.edge_index)
+            prev = row[0]
+
+            final_hv = torchhd.empty(1, self.out_features, VSA)
+            aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            for idx in range(len(x.edge_index[0])):
+                i = x.edge_index[0][idx]
+                j = x.edge_index[1][idx]
+                if prev == i:
+                    if has_attr:
+                        aux_hv = torchhd.bind(
+                            aux_hv,
+                            torchhd.bind(
+                                torchhd.bind(node_id_hvs[i], node_id_hvs[j]),
+                                edge_attr[idx],
+                            ),
+                        )
+                    else:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                else:
+                    prev = i
+                    final_hv = torchhd.bundle(final_hv, aux_hv)
+                    if has_attr:
+                        aux_hv = torchhd.bind(
+                            torchhd.bind(node_id_hvs[i], node_id_hvs[j]), edge_attr[idx]
+                        )
+                    else:
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+
+            return final_hv[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+DATASET = ["AIDS", "BZR", "COX2", "DHFR", "FRANKENSTEIN"]
+
+# ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_rank.py b/GraphHD_v2/graphhd_list_rank.py
new file mode 100644
index 00000000..dcf08d3a
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_rank.py
@@ -0,0 +1,276 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_rank/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]  # ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+with open(csv_file, mode="a", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(
+        [
+            "dataset",
+            "dimensions",
+            "train_time",
+            "test_time",
+            "accuracy",
+            "f1",
+            "VSA",
+        ]
+    )
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_rank_node_attr.py b/GraphHD_v2/graphhd_list_rank_node_attr.py
new file mode 100644
index 00000000..e07b6d20
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_rank_node_attr.py
@@ -0,0 +1,318 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_rank_node/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            # node_id_hvs = self.node_ids.weight[: x.num_nodes]
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            indices_tensor = torch.stack(
+                [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+            )
+            node_attr = self.node_attr.weight[indices_tensor]
+            node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            prev = row[0]
+
+            final_hv = torchhd.empty(1, self.out_features, VSA)
+            aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            for idx in range(len(x.edge_index[0])):
+                i = x.edge_index[0][idx]
+                j = x.edge_index[1][idx]
+                if prev == i:
+                    aux_hv = torchhd.bind(
+                        aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                    )
+                else:
+                    prev = i
+                    final_hv = torchhd.bundle(final_hv, aux_hv)
+                    aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+            return final_hv[0]
+
+        def forward_hashmap_label_random(self, x):
+            node_id_hvs = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            if len(x.x[0]) > 0:
+                indices_tensor = torch.stack(
+                    [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+                )
+                node_attr = self.node_attr.weight[indices_tensor]
+                node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]  # ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_list_rank_node_attr2.py b/GraphHD_v2/graphhd_list_rank_node_attr2.py
new file mode 100644
index 00000000..7a4ebc29
--- /dev/null
+++ b/GraphHD_v2/graphhd_list_rank_node_attr2.py
@@ -0,0 +1,285 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+
+import time
+
+csv_file = "list_rank_node2/result" + str(time.time()) + ".csv"
+DIM = 10000
+import networkx as nx
+from torch_geometric.utils import to_networkx
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+        return v
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores, scores_nodes
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, num_node_attr):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+            self.levels = embeddings.Level(size, out_features, vsa=VSA)
+            self.node_attr = embeddings.Random(num_node_attr, out_features, vsa=VSA)
+
+        def forward(self, x):
+            # node_id_hvs = self.node_ids.weight[: x.num_nodes]
+            pr = pagerank(x)
+            pr_sort, pr_argsort = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[pr_argsort] = self.node_ids.weight[: x.num_nodes]
+
+            def index_value(inner_tensor):
+                return torch.argmax(inner_tensor)
+
+            indices_tensor = torch.stack(
+                [index_value(inner_tensor) for inner_tensor in x.x.unbind()]
+            )
+            node_attr = self.node_attr.weight[indices_tensor]
+            node_id_hvs = torchhd.bind(node_id_hvs, node_attr)
+
+            row, col = to_undirected(x.edge_index)
+            if len(row) > 0:
+                prev = row[0]
+
+                final_hv = torchhd.empty(1, self.out_features, VSA)
+                aux_hv = torchhd.identity(1, self.out_features, VSA)
+
+                for idx in range(len(x.edge_index[0])):
+                    i = x.edge_index[0][idx]
+                    j = x.edge_index[1][idx]
+                    if prev == i:
+                        aux_hv = torchhd.bind(
+                            aux_hv, torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                        )
+                    else:
+                        prev = i
+                        final_hv = torchhd.bundle(final_hv, aux_hv)
+                        aux_hv = torchhd.bind(node_id_hvs[i], node_id_hvs[j])
+                return final_hv[0]
+
+            else:
+                return torchhd.empty(1, self.out_features, VSA)[0]
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, graphs.num_node_features)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for i, samples in enumerate(tqdm(train_ld, desc="Training")):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+
+            model.add(samples_hv, samples.y)
+            # break
+
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            # break
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=False)
+
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 10
+RANDOMNESS = ["random"]
+DATASET = [
+    "AIDS",
+    "BZR",
+    "BZR_MD",
+    "COX2",
+    "COX2_MD",
+    "DHFR",
+    "DHFR_MD",
+    "ER_MD",
+    "FRANKENSTEIN",
+    "MCF-7",
+    "MCF-7H",
+    "MOLT-4",
+    "MOLT-4H",
+    "Mutagenicity",
+    "MUTAG",
+    "NCI1",
+    "NCI109",
+    "NCI-H23",
+    "NCI-H23H",
+    "OVCAR-8",
+    "OVCAR-8H",
+    "P388",
+    "P388H",
+    "PC-3",
+    "PC-3H",
+    "PTC_FM",
+    "PTC_FR",
+    "PTC_MM",
+    "PTC_MR",
+    "SF-295",
+    "SF-295H",
+    "SN12C",
+    "SN12CH",
+    "SW-620",
+    "SW-620H",
+    "UACC257",
+    "UACC257H",
+    "Yeast",
+    "YeastH",
+]  # ,'BZR_MD','COX2','COX2_MD','DHFR','DHFR_MD','ER_MD', 'FRANKENSTEIN', 'NCI109','KKI','OHSU','Peking_1','PROTEINS','AIDS']
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        acc_final = []
+        f1_final = []
+        train_final = []
+        test_final = []
+
+        for i in RANDOMNESS:
+            acc_aux = []
+            f1_aux = []
+            train_aux = []
+            test_aux = []
+            for j in range(REPETITIONS):
+                acc, f1, train_t, test_t = experiment(1, i, d)
+                acc_aux.append(acc)
+                f1_aux.append(f1)
+                train_aux.append(train_t)
+                test_aux.append(test_t)
+            acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+            f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+            train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+            test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+        with open(csv_file, mode="a", newline="") as file:
+            writer = csv.writer(file)
+            writer.writerow(
+                [
+                    "dataset",
+                    "dimensions",
+                    "train_time",
+                    "test_time",
+                    "accuracy",
+                    "f1",
+                    "VSA",
+                ]
+            )
+            writer.writerows(
+                [
+                    [
+                        d,
+                        DIM,
+                        train_final[0],
+                        test_final[0],
+                        acc_final[0],
+                        f1_final[0],
+                        VSA,
+                    ]
+                ]
+            )
diff --git a/GraphHD_v2/graphhd_std_centrality.py b/GraphHD_v2/graphhd_std_centrality.py
new file mode 100644
index 00000000..d8987243
--- /dev/null
+++ b/GraphHD_v2/graphhd_std_centrality.py
@@ -0,0 +1,524 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+from torch_geometric.datasets import TUDataset
+from torch_geometric.utils import to_networkx
+from torch_geometric.data import DataLoader
+from torch_geometric.utils.degree import degree
+import networkx as nx
+
+# Note: this example requires the torch_geometric library: https://pytorch-geometric.readthedocs.io
+from torch_geometric.datasets import TUDataset
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+
+import torchhd
+from torchhd import embeddings
+from torchhd.models import Centroid
+import csv
+from torch_geometric.utils import to_networkx
+import networkx as nx
+import numpy as np
+import time
+
+csv_file = "metrics/result" + str(time.time()) + ".csv"
+DIM = 10000
+
+
+def experiment(randomness=0, embed="random", dataset="MUTAG", metric="page_rank"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Using {} device".format(device))
+
+    DIMENSIONS = DIM  # hypervectors dimension
+
+    # for other available datasets see: https://pytorch-geometric.readthedocs.io/en/latest/notes/data_cheatsheet.html?highlight=tudatasets
+    # dataset = "MUTAG"
+
+    graphs = TUDataset("../data", dataset)
+    train_size = int(0.7 * len(graphs))
+    test_size = len(graphs) - train_size
+    train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
+
+    def sparse_stochastic_graph(G):
+        """
+        Returns a sparse adjacency matrix of the graph G.
+        The values indicate the probability of leaving a vertex.
+        This means that each column sums up to one.
+        """
+        rows, columns = G.edge_index
+        # Calculate the probability for each column
+        values_per_column = 1.0 / torch.bincount(columns, minlength=G.num_nodes)
+        values_per_node = values_per_column[columns]
+        size = (G.num_nodes, G.num_nodes)
+        return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
+
+    def centrality(data):
+        degree_centrality = data.edge_index[0].bincount(minlength=data.num_nodes)
+        degree_ranked_nodes = sorted(
+            range(data.num_nodes), key=lambda node: degree_centrality[node]
+        )
+
+    def semi_local_centrality(data):
+        G = nx.Graph()
+
+        for i in range(data.edge_index.size(1)):
+            edge = tuple(data.edge_index[:, i].tolist())
+            G.add_edge(*edge)
+
+        # Calculate semi-local centrality using a custom approach
+        semi_local_centrality = []
+
+        for node in G.nodes():
+            ego_graph = nx.ego_graph(
+                G, node, radius=2
+            )  # Adjust the radius (2 in this case)
+            semi_local_centrality.append(len(ego_graph))
+
+        # Store the semi-local centrality scores in the PyTorch Geometric Data object
+        data.semi_local_centrality = torch.tensor(semi_local_centrality)
+
+        # Rank nodes based on semi-local centrality
+        semi_local_ranked_nodes = sorted(
+            G.nodes(), key=lambda node: semi_local_centrality[node]
+        )
+        return semi_local_ranked_nodes
+
+    def degree_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.degree_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def eigen_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.eigenvector_centrality(G, max_iter=1000)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def katz_centrality(data):
+        G = to_networkx(data)
+
+        beta = 0.1
+        scores = nx.katz_centrality(G, beta=beta)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def closeness_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.closeness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def incremental_closeness_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+        G.add_edges_from(data.edge_index.t().tolist())
+
+        scores = nx.incremental_closeness_centrality(G, G.edges())
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def current_flow_closeness_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+        scores = nx.current_flow_closeness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def information_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+        scores = nx.information_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def betweenness_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.betweenness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def edge_betweenness_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.edge_betweenness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def current_flow_betweeness_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.current_flow_closeness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def edge_current_flow_betweeness_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.edge_current_flow_betweenness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def communicability_betweeness_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.communicability_betweenness_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def load_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.load_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def edge_load_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.edge_load_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def subgraph_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.subgraph_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def subgraph_centrality_exp(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.subgraph_centrality_exp(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def estrada_index(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.estrada_index(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def harmonic_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.harmonic_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def dispersion(data):
+        G = to_networkx(data)
+
+        scores = nx.dispersion(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def global_reaching_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.global_reaching_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def percolation_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.percolation_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def second_order_centrality(data):
+        G = to_networkx(data)
+        G = G.to_undirected()
+
+        scores = nx.second_order_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def trophic_levels(data):
+        G = to_networkx(data)
+
+        scores = nx.trophic_levels(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def trophic_differences(data):
+        G = to_networkx(data)
+
+        scores = nx.trophic_differences(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def trophic_incoherence_parameter(data):
+        G = to_networkx(data)
+
+        scores = nx.trophic_incoherence_parameter(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def voterank(data):
+        G = to_networkx(data)
+
+        scores = nx.voterank(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def laplacian_centrality(data):
+        G = to_networkx(data)
+
+        scores = nx.laplacian_centrality(G)
+        scores_nodes = sorted(G.nodes(), key=lambda node: scores[node])
+
+        return scores_nodes
+
+    def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
+        N = G.num_nodes
+        M = sparse_stochastic_graph(G) * alpha
+        v = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        p = torch.zeros(N, device=G.edge_index.device) + 1 / N
+        for _ in range(max_iter):
+            v_prev = v
+            v = M @ v + p * (1 - alpha)
+
+            err = (v - v_prev).abs().sum()
+            if tol != None and err < N * tol:
+                return v
+
+        return v
+
+    def to_undirected(edge_index):
+        """
+        Returns the undirected edge_index
+        [[0, 1], [1, 0]] will result in [[0], [1]]
+        """
+        edge_index = edge_index.sort(dim=0)[0]
+        edge_index = torch.unique(edge_index, dim=1)
+        return edge_index
+
+    def min_max_graph_size(graph_dataset):
+        if len(graph_dataset) == 0:
+            return None, None
+
+        max_num_nodes = float("-inf")
+        min_num_nodes = float("inf")
+
+        for G in graph_dataset:
+            num_nodes = G.num_nodes
+            max_num_nodes = max(max_num_nodes, num_nodes)
+            min_num_nodes = min(min_num_nodes, num_nodes)
+
+        return min_num_nodes, max_num_nodes
+
+    class Encoder(nn.Module):
+        def __init__(self, out_features, size, metric):
+            super(Encoder, self).__init__()
+            self.out_features = out_features
+            self.metric = metric
+            if embed == "thermometer":
+                self.node_ids = embeddings.Thermometer(size, out_features, vsa=VSA)
+            elif embed == "circular":
+                self.node_ids = embeddings.Circular(size, out_features, vsa=VSA)
+            elif embed == "projection":
+                self.node_ids = embeddings.Projection(size, out_features, vsa=VSA)
+            elif embed == "sinusoid":
+                self.node_ids = embeddings.Sinusoid(size, out_features, vsa=VSA)
+            elif embed == "density":
+                self.node_ids = embeddings.Density(size, out_features, vsa=VSA)
+            else:
+                self.node_ids = embeddings.Random(size, out_features, vsa=VSA)
+
+        def forward(self, x):
+            if metric == "degree_centrality":
+                order = degree_centrality(x)
+            elif metric == "eigen_centrality":
+                order = eigen_centrality(x)
+            elif metric == "katz_centrality":
+                order = katz_centrality(x)
+            elif metric == "closeness_centrality":
+                order = closeness_centrality(x)
+            elif metric == "current_flow_closeness_centrality":
+                order = current_flow_closeness_centrality(x)
+            elif metric == "information_centrality":
+                order = information_centrality(x)
+            elif metric == "betweenness_centrality":
+                order = betweenness_centrality(x)
+            elif metric == "current_flow_betweeness_centrality":
+                order = current_flow_betweeness_centrality(x)
+            elif metric == "communicability_betweeness_centrality":
+                order = communicability_betweeness_centrality(x)
+            elif metric == "load_centrality":
+                order = load_centrality(x)
+            elif metric == "subgraph_centrality":
+                order = subgraph_centrality(x)
+            elif metric == "subgraph_centrality_exp":
+                order = subgraph_centrality_exp(x)
+            elif metric == "harmonic_centrality":
+                order = harmonic_centrality(x)
+            elif metric == "second_order_centrality":
+                order = second_order_centrality(x)
+            elif metric == "trophic_levels":
+                order = trophic_levels(x)
+            elif metric == "laplacian_centrality":
+                order = laplacian_centrality(x)
+            elif metric == "none":
+                order = list(range(x.num_nodes))
+            else:
+                pr = pagerank(x)
+                pr_sort, order = pr.sort()
+
+            node_id_hvs = torchhd.empty(x.num_nodes, self.out_features, VSA)
+            node_id_hvs[order] = self.node_ids.weight[: x.num_nodes]
+
+            row, col = to_undirected(x.edge_index)
+
+            hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
+            return torchhd.multiset(hvs)
+
+    min_graph_size, max_graph_size = min_max_graph_size(graphs)
+    encode = Encoder(DIMENSIONS, max_graph_size, metric)
+    encode = encode.to(device)
+
+    model = Centroid(DIMENSIONS, graphs.num_classes, VSA)
+    model = model.to(device)
+
+    train_t = time.time()
+    with torch.no_grad():
+        for samples in tqdm(train_ld, desc="Training"):
+            samples.edge_index = samples.edge_index.to(device)
+            samples.y = samples.y.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            model.add(samples_hv, samples.y)
+    train_t = time.time() - train_t
+    accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+
+    test_t = time.time()
+    with torch.no_grad():
+        if VSA != "BSC":
+            model.normalize()
+
+        for samples in tqdm(test_ld, desc="Testing"):
+            samples.edge_index = samples.edge_index.to(device)
+
+            samples_hv = encode(samples).unsqueeze(0)
+            outputs = model(samples_hv, dot=True)
+            accuracy.update(outputs.cpu(), samples.y)
+            f1.update(outputs.cpu(), samples.y)
+    test_t = time.time() - test_t
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
+    return acc, f, train_t, test_t
+
+
+REPETITIONS = 1
+RANDOMNESS = ["random"]
+# DATASET = ["PTC_FM", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+METRICS = [
+    "none",
+    "page_rank",
+    "degree_centrality",
+    "closeness_centrality",
+    "betweenness_centrality",
+    "load_centrality",
+    "subgraph_centrality",
+    "subgraph_centrality_exp",
+    "harmonic_centrality",
+]
+DATASET = ["PTC_FM", "MUTAG", "NCI1", "ENZYMES", "PROTEINS", "DD"]
+# VSAS = ["BSC", "MAP", "HRR", "FHRR"]
+VSAS = ["FHRR"]
+
+
+for VSA in VSAS:
+    for d in DATASET:
+        for METRIC in METRICS:
+            acc_final = []
+            f1_final = []
+            train_final = []
+            test_final = []
+            for i in RANDOMNESS:
+                acc_aux = []
+                f1_aux = []
+                train_aux = []
+                test_aux = []
+                for j in range(REPETITIONS):
+                    acc, f1, train_t, test_t = experiment(1, i, d, METRIC)
+                    acc_aux.append(acc)
+                    f1_aux.append(f1)
+                    train_aux.append(train_t)
+                    test_aux.append(test_t)
+                acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+                f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
+                train_final.append(round(sum(train_aux) / REPETITIONS, 2))
+                test_final.append(round(sum(test_aux) / REPETITIONS, 2))
+
+            with open(csv_file, mode="a", newline="") as file:
+                writer = csv.writer(file)
+                writer.writerow(
+                    [
+                        "dataset",
+                        "dimensions",
+                        "train_time",
+                        "test_time",
+                        "accuracy",
+                        "f1",
+                        "VSA",
+                        "metric",
+                    ]
+                )
+                writer.writerows(
+                    [
+                        [
+                            d,
+                            DIM,
+                            train_final[0],
+                            test_final[0],
+                            acc_final[0],
+                            f1_final[0],
+                            VSA,
+                            METRIC,
+                        ]
+                    ]
+                )
diff --git a/GraphHD_v2/level/result1699578347.486874.csv b/GraphHD_v2/level/result1699578347.486874.csv
new file mode 100644
index 00000000..da7e55a6
--- /dev/null
+++ b/GraphHD_v2/level/result1699578347.486874.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.04,0.0,0.0,0.0,FHRR
diff --git a/GraphHD_v2/level/result1699578431.389778.csv b/GraphHD_v2/level/result1699578431.389778.csv
new file mode 100644
index 00000000..c79187db
--- /dev/null
+++ b/GraphHD_v2/level/result1699578431.389778.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.02,0.0,0.0,0.0,FHRR
diff --git a/GraphHD_v2/level/result1699578442.387155.csv b/GraphHD_v2/level/result1699578442.387155.csv
new file mode 100644
index 00000000..c79187db
--- /dev/null
+++ b/GraphHD_v2/level/result1699578442.387155.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.02,0.0,0.0,0.0,FHRR
diff --git a/GraphHD_v2/level/result1699578688.829068.csv b/GraphHD_v2/level/result1699578688.829068.csv
new file mode 100644
index 00000000..da7e55a6
--- /dev/null
+++ b/GraphHD_v2/level/result1699578688.829068.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+ENZYMES,10000,0.04,0.0,0.0,0.0,FHRR
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index 00000000..a9023447
--- /dev/null
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index 00000000..c79187db
--- /dev/null
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index 00000000..c79187db
--- /dev/null
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--- /dev/null
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--- /dev/null
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index 00000000..07352c54
--- /dev/null
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new file mode 100644
index 00000000..613f7280
--- /dev/null
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index 00000000..ac6f275f
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--- /dev/null
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--- /dev/null
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index 00000000..c79187db
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index 00000000..f6367790
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index 00000000..824195ac
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--- /dev/null
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index 00000000..cfdd29e7
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index 00000000..068a26ae
--- /dev/null
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index 00000000..3500f45f
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index 00000000..82560db0
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--- /dev/null
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--- /dev/null
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--- /dev/null
+++ b/GraphHD_v2/level/result1699593977.545095.csv
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--- /dev/null
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index 00000000..cdd30916
--- /dev/null
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index 00000000..c7305da2
--- /dev/null
+++ b/GraphHD_v2/level/result1699594041.001162.csv
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index 00000000..dee32089
--- /dev/null
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--- /dev/null
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index 00000000..c79187db
--- /dev/null
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index 00000000..ad73dd2a
--- /dev/null
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new file mode 100644
index 00000000..c79187db
--- /dev/null
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index 00000000..ad73dd2a
--- /dev/null
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index 00000000..ad73dd2a
--- /dev/null
+++ b/GraphHD_v2/level/result1699595482.579198.csv
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new file mode 100644
index 00000000..ad73dd2a
--- /dev/null
+++ b/GraphHD_v2/level/result1699595524.909657.csv
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new file mode 100644
index 00000000..ad73dd2a
--- /dev/null
+++ b/GraphHD_v2/level/result1699596259.9128609.csv
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new file mode 100644
index 00000000..da7e55a6
--- /dev/null
+++ b/GraphHD_v2/level/result1699596287.1179352.csv
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new file mode 100644
index 00000000..c79187db
--- /dev/null
+++ b/GraphHD_v2/level/result1699596316.49761.csv
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new file mode 100644
index 00000000..da7e55a6
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index 00000000..da7e55a6
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new file mode 100644
index 00000000..ad73dd2a
--- /dev/null
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index 00000000..ad73dd2a
--- /dev/null
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index 00000000..a9023447
--- /dev/null
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index 00000000..9390f178
--- /dev/null
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index 00000000..916aebbd
--- /dev/null
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new file mode 100644
index 00000000..15e23756
--- /dev/null
+++ b/GraphHD_v2/level/result1699762784.2433422.csv
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new file mode 100644
index 00000000..31834fe8
--- /dev/null
+++ b/GraphHD_v2/level/result1699762804.07495.csv
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index 00000000..47a7bd6f
--- /dev/null
+++ b/GraphHD_v2/level/result1699762846.623924.csv
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index 00000000..0bee8cb2
--- /dev/null
+++ b/GraphHD_v2/level/result1699762925.826139.csv
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new file mode 100644
index 00000000..15e23756
--- /dev/null
+++ b/GraphHD_v2/level/result1699763155.7063282.csv
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new file mode 100644
index 00000000..0d96f29d
--- /dev/null
+++ b/GraphHD_v2/level/result1699763186.573164.csv
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new file mode 100644
index 00000000..abb511e7
--- /dev/null
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index 00000000..0d96f29d
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+++ b/GraphHD_v2/level/result1699763839.2594318.csv
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index 00000000..0d96f29d
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+++ b/GraphHD_v2/level/result1699763951.6192908.csv
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index 00000000..0d96f29d
--- /dev/null
+++ b/GraphHD_v2/level/result1699764147.784245.csv
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new file mode 100644
index 00000000..15e23756
--- /dev/null
+++ b/GraphHD_v2/level/result1699764159.788817.csv
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new file mode 100644
index 00000000..abb511e7
--- /dev/null
+++ b/GraphHD_v2/level/result1699764170.1761541.csv
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new file mode 100644
index 00000000..abb511e7
--- /dev/null
+++ b/GraphHD_v2/level/result1699764220.84833.csv
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diff --git a/GraphHD_v2/level/result1699764305.088311.csv b/GraphHD_v2/level/result1699764305.088311.csv
new file mode 100644
index 00000000..d5faba8d
--- /dev/null
+++ b/GraphHD_v2/level/result1699764305.088311.csv
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diff --git a/GraphHD_v2/level/result1699764392.114508.csv b/GraphHD_v2/level/result1699764392.114508.csv
new file mode 100644
index 00000000..95aa55f6
--- /dev/null
+++ b/GraphHD_v2/level/result1699764392.114508.csv
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new file mode 100644
index 00000000..9ca80773
--- /dev/null
+++ b/GraphHD_v2/level/result1699764460.891639.csv
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new file mode 100644
index 00000000..76d312d2
--- /dev/null
+++ b/GraphHD_v2/level/result1699764552.618688.csv
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diff --git a/GraphHD_v2/level/result1699764626.042729.csv b/GraphHD_v2/level/result1699764626.042729.csv
new file mode 100644
index 00000000..e75e10fd
--- /dev/null
+++ b/GraphHD_v2/level/result1699764626.042729.csv
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diff --git a/GraphHD_v2/level/result1699765161.837012.csv b/GraphHD_v2/level/result1699765161.837012.csv
new file mode 100644
index 00000000..880c49a4
--- /dev/null
+++ b/GraphHD_v2/level/result1699765161.837012.csv
@@ -0,0 +1,12 @@
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diff --git a/GraphHD_v2/level/result1699765853.11426.csv b/GraphHD_v2/level/result1699765853.11426.csv
new file mode 100644
index 00000000..314d03bc
--- /dev/null
+++ b/GraphHD_v2/level/result1699765853.11426.csv
@@ -0,0 +1,12 @@
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diff --git a/GraphHD_v2/level/result1699894465.8592472.csv b/GraphHD_v2/level/result1699894465.8592472.csv
new file mode 100644
index 00000000..d287dcae
--- /dev/null
+++ b/GraphHD_v2/level/result1699894465.8592472.csv
@@ -0,0 +1,12 @@
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diff --git a/GraphHD_v2/level/result1699895812.510516.csv b/GraphHD_v2/level/result1699895812.510516.csv
new file mode 100644
index 00000000..7629e12a
--- /dev/null
+++ b/GraphHD_v2/level/result1699895812.510516.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/level/result1699895929.36012.csv b/GraphHD_v2/level/result1699895929.36012.csv
new file mode 100644
index 00000000..7629e12a
--- /dev/null
+++ b/GraphHD_v2/level/result1699895929.36012.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/level/result1699896021.45926.csv b/GraphHD_v2/level/result1699896021.45926.csv
new file mode 100644
index 00000000..4be8f38f
--- /dev/null
+++ b/GraphHD_v2/level/result1699896021.45926.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/level/result1699896058.510886.csv b/GraphHD_v2/level/result1699896058.510886.csv
new file mode 100644
index 00000000..7629e12a
--- /dev/null
+++ b/GraphHD_v2/level/result1699896058.510886.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/level/result1699896171.076137.csv b/GraphHD_v2/level/result1699896171.076137.csv
new file mode 100644
index 00000000..d568b9e5
--- /dev/null
+++ b/GraphHD_v2/level/result1699896171.076137.csv
@@ -0,0 +1,2 @@
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diff --git a/GraphHD_v2/level/result1699896229.802478.csv b/GraphHD_v2/level/result1699896229.802478.csv
new file mode 100644
index 00000000..677c636a
--- /dev/null
+++ b/GraphHD_v2/level/result1699896229.802478.csv
@@ -0,0 +1,58 @@
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diff --git a/GraphHD_v2/level/result1699896482.4919229.csv b/GraphHD_v2/level/result1699896482.4919229.csv
new file mode 100644
index 00000000..5ab74a79
--- /dev/null
+++ b/GraphHD_v2/level/result1699896482.4919229.csv
@@ -0,0 +1,20 @@
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diff --git a/GraphHD_v2/list_basic_node2/result1699913335.2692878.csv b/GraphHD_v2/list_basic_node2/result1699913335.2692878.csv
new file mode 100644
index 00000000..82dad0a5
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699913335.2692878.csv
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diff --git a/GraphHD_v2/list_basic_node2/result1699913351.271691.csv b/GraphHD_v2/list_basic_node2/result1699913351.271691.csv
new file mode 100644
index 00000000..8913a230
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699913351.271691.csv
@@ -0,0 +1,12 @@
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+ER_MD,10000,6.2,3.31,62.69,62.69,FHRR
+FRANKENSTEIN,10000,4.74,4.33,61.44,61.44,FHRR
+MCF-7,10000,72.68,31.14,78.5,78.5,FHRR
+MCF-7H,10000,101.0,46.87,70.98,70.98,FHRR
diff --git a/GraphHD_v2/list_basic_node2/result1699914443.615314.csv b/GraphHD_v2/list_basic_node2/result1699914443.615314.csv
new file mode 100644
index 00000000..82dad0a5
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699914443.615314.csv
@@ -0,0 +1 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
diff --git a/GraphHD_v2/list_basic_node2/result1699914547.9685602.csv b/GraphHD_v2/list_basic_node2/result1699914547.9685602.csv
new file mode 100644
index 00000000..82dad0a5
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699914547.9685602.csv
@@ -0,0 +1 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
diff --git a/GraphHD_v2/list_basic_node2/result1699914583.4597728.csv b/GraphHD_v2/list_basic_node2/result1699914583.4597728.csv
new file mode 100644
index 00000000..82dad0a5
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699914583.4597728.csv
@@ -0,0 +1 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
diff --git a/GraphHD_v2/list_basic_node2/result1699914660.2943099.csv b/GraphHD_v2/list_basic_node2/result1699914660.2943099.csv
new file mode 100644
index 00000000..4b3750a0
--- /dev/null
+++ b/GraphHD_v2/list_basic_node2/result1699914660.2943099.csv
@@ -0,0 +1,29 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+MOLT-4,10000,93.31,44.41,74.76,74.76,FHRR
+MOLT-4H,10000,147.44,66.59,69.82,69.82,FHRR
+Mutagenicity,10000,9.9,6.32,68.13,68.13,FHRR
+MUTAG,10000,0.27,0.15,78.95,78.95,FHRR
+NCI1,10000,9.85,4.53,65.45,65.45,FHRR
+NCI109,10000,9.52,4.51,64.89,64.89,FHRR
+NCI-H23,10000,85.36,40.34,81.69,81.69,FHRR
+NCI-H23H,10000,145.81,66.12,73.44,73.44,FHRR
+OVCAR-8,10000,82.35,39.76,80.58,80.58,FHRR
+OVCAR-8H,10000,144.6,65.45,70.4,70.4,FHRR
+P388,10000,71.2,34.87,81.17,81.17,FHRR
+P388H,10000,128.39,58.76,74.36,74.36,FHRR
+PC-3,10000,57.4,27.54,80.83,80.83,FHRR
+PC-3H,10000,100.01,44.83,75.31,75.31,FHRR
+PTC_FM,10000,0.37,0.21,58.1,58.1,FHRR
+PTC_FR,10000,0.4,0.2,57.55,57.55,FHRR
+PTC_MM,10000,0.36,0.19,69.31,69.31,FHRR
+PTC_MR,10000,0.38,0.21,51.92,51.92,FHRR
+SF-295,10000,82.4,39.88,80.98,80.98,FHRR
+SF-295H,10000,142.86,64.57,73.16,73.16,FHRR
+SN12C,10000,81.06,38.81,81.1,81.1,FHRR
+SN12CH,10000,144.62,65.77,71.46,71.46,FHRR
+SW-620,10000,82.96,39.64,78.35,78.35,FHRR
+SW-620H,10000,145.85,66.82,69.69,69.69,FHRR
+UACC257,10000,82.37,40.06,82.55,82.55,FHRR
+UACC257H,10000,140.79,65.45,73.89,73.89,FHRR
+Yeast,10000,133.86,65.9,61.5,61.5,FHRR
+YeastH,10000,238.76,109.82,64.41,64.41,FHRR
diff --git a/GraphHD_v2/metrics/result1699489987.410995.csv b/GraphHD_v2/metrics/result1699489987.410995.csv
new file mode 100644
index 00000000..4af0e31a
--- /dev/null
+++ b/GraphHD_v2/metrics/result1699489987.410995.csv
@@ -0,0 +1,108 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.18,0.09,54.29,54.29,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.28,0.15,54.29,54.29,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.16,0.1,57.14,57.14,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.21,0.11,66.67,66.67,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.27,0.14,56.19,56.19,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.31,0.15,55.24,55.24,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,1.86,1.04,56.19,56.19,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,2.01,0.43,58.1,58.1,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PTC_FM,10000,0.34,0.12,57.14,57.14,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.1,0.06,84.21,84.21,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.18,0.11,84.21,84.21,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.11,0.06,84.21,84.21,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.15,0.07,87.72,87.72,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.16,0.08,80.7,80.7,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.15,0.08,84.21,84.21,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.33,0.17,84.21,84.21,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.84,0.32,80.7,80.7,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+MUTAG,10000,0.29,0.08,87.72,87.72,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,3.23,1.65,61.56,61.56,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.19,2.66,63.99,63.99,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,3.92,2.12,61.96,61.96,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,5.31,2.7,64.88,64.88,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,6.88,3.1,63.34,63.34,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,6.87,3.33,63.75,63.75,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,17.5,7.46,63.99,63.99,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,14.78,7.28,62.69,62.69,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+NCI1,10000,4.73,2.33,62.21,62.21,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.79,0.37,38.33,38.33,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.86,0.44,25.56,25.56,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,0.82,0.39,17.78,17.78,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.24,0.7,22.78,22.78,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.46,0.68,25.56,25.56,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.41,0.66,23.33,23.33,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,3.16,1.46,31.67,31.67,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,3.93,1.93,22.22,22.22,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+ENZYMES,10000,1.46,0.5,27.22,27.22,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.78,0.79,67.37,67.37,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.91,0.99,70.66,70.66,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,1.82,1.01,70.66,70.66,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,2.77,1.17,67.37,67.37,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,4.91,1.96,66.17,66.17,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,4.35,2.03,68.86,68.86,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,7.11,9.03,70.06,70.06,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,38.65,14.57,68.86,68.86,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+PROTEINS,10000,2.62,1.36,65.57,65.57,FHRR,harmonic_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,13.78,8.62,70.34,70.34,FHRR,none
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,14.7,7.09,75.71,75.71,FHRR,page_rank
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,18.09,6.54,73.16,73.16,FHRR,degree_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,41.61,34.44,73.73,73.73,FHRR,closeness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,218.13,55.61,74.01,74.01,FHRR,betweenness_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,196.91,53.53,73.73,73.73,FHRR,load_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,79.52,21.22,73.45,73.45,FHRR,subgraph_centrality
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,460.37,188.7,75.14,75.14,FHRR,subgraph_centrality_exp
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA,metric
+DD,10000,72.85,21.18,73.16,73.16,FHRR,harmonic_centrality
diff --git a/GraphHD_v2/node_attr/result1699904378.53636.csv b/GraphHD_v2/node_attr/result1699904378.53636.csv
new file mode 100644
index 00000000..766e7a77
--- /dev/null
+++ b/GraphHD_v2/node_attr/result1699904378.53636.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,3.18,1.09,74.67,74.67,FHRR
diff --git a/GraphHD_v2/node_attr/result1699904412.549875.csv b/GraphHD_v2/node_attr/result1699904412.549875.csv
new file mode 100644
index 00000000..3d1a5c9e
--- /dev/null
+++ b/GraphHD_v2/node_attr/result1699904412.549875.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,1.9,1.02,71.17,71.17,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.86,0.41,81.15,81.15,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,1.12,0.52,59.57,59.57,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,1.86,0.87,77.97,77.97,FHRR
diff --git a/GraphHD_v2/node_attr/result1699904503.967231.csv b/GraphHD_v2/node_attr/result1699904503.967231.csv
new file mode 100644
index 00000000..25781d71
--- /dev/null
+++ b/GraphHD_v2/node_attr/result1699904503.967231.csv
@@ -0,0 +1,8 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,2.09,1.03,69.17,69.17,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.98,0.6,78.69,78.69,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,2.09,0.6,70.92,70.92,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,1.96,0.88,71.37,71.37,FHRR
diff --git a/GraphHD_v2/node_attr/result1699910347.078403.csv b/GraphHD_v2/node_attr/result1699910347.078403.csv
new file mode 100644
index 00000000..3ed9d045
--- /dev/null
+++ b/GraphHD_v2/node_attr/result1699910347.078403.csv
@@ -0,0 +1,2 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+FRANKENSTEIN,10000,4.39,2.19,61.9,61.9,FHRR
diff --git a/GraphHD_v2/node_attr/result1699910477.3889751.csv b/GraphHD_v2/node_attr/result1699910477.3889751.csv
new file mode 100644
index 00000000..315021ff
--- /dev/null
+++ b/GraphHD_v2/node_attr/result1699910477.3889751.csv
@@ -0,0 +1,10 @@
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+AIDS,10000,3.38,1.63,93.5,93.5,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+BZR,10000,0.99,0.48,81.97,81.97,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+COX2,10000,1.27,0.6,64.54,64.54,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+DHFR,10000,2.27,1.05,74.01,74.01,FHRR
+dataset,dimensions,train_time,test_time,accuracy,f1,VSA
+FRANKENSTEIN,10000,7.22,4.32,62.37,62.37,FHRR
diff --git a/examples/graphhd.py b/examples/graphhd.py
index fa86bd08..acba9c24 100644
--- a/examples/graphhd.py
+++ b/examples/graphhd.py
@@ -85,7 +85,7 @@ class Encoder(nn.Module):
     def __init__(self, out_features, size):
         super(Encoder, self).__init__()
         self.out_features = out_features
-        self.node_ids = embeddings.Random(size, out_features)
+        self.node_ids = embeddings.Level(size, out_features, randomness=0.05)
 
     def forward(self, x):
         pr = pagerank(x)
diff --git a/torchhd/models.py b/torchhd/models.py
index 0f7de6dc..782504b5 100644
--- a/torchhd/models.py
+++ b/torchhd/models.py
@@ -108,6 +108,39 @@ def add(self, input: Tensor, target: Tensor, lr: float = 1.0) -> None:
         """Adds the input vectors scaled by the lr to the target prototype vectors."""
         self.weight.index_add_(0, target, input, alpha=lr)
 
+    @torch.no_grad()
+    def add_refine(self, input: Tensor, target: Tensor, lr: float = 1.0) -> None:
+        logit = self(input)
+        predx = torch.topk(logit, 2)
+        pred = torch.tensor([predx.indices[0][0]])
+        is_wrong = target != pred
+
+        alpha = 1 - (abs(predx[0][0][0]) - abs(predx[0][0][1]))
+
+        self.similarity_sum += logit.max(1).values.item()
+        self.count += 1
+        if self.error_count == 0:
+            val = self.similarity_sum / self.count
+        else:
+            val = self.error_similarity_sum / self.error_count
+        if is_wrong.sum().item() == 0:
+            if logit.max(1).values.item() < val:
+                self.weight.index_add_(0, target, lr * alpha * input)
+            return
+
+        self.error_count += 1
+        self.error_similarity_sum += logit.max(1).values.item()
+
+        logit = logit[is_wrong]
+        input = input[is_wrong]
+        target = target[is_wrong]
+        pred = pred[is_wrong]
+        alpha1 = 1.0 - logit.gather(1, target.unsqueeze(1))
+        alpha2 = logit.gather(1, pred.unsqueeze(1)) - 1
+
+        self.weight.index_add_(0, target, lr * alpha1 * alpha * input)
+        self.weight.index_add_(0, pred, lr * alpha2 * alpha * input)
+
     @torch.no_grad()
     def add_online(self, input: Tensor, target: Tensor, lr: float = 1.0) -> None:
         r"""Only updates the prototype vectors on wrongly predicted inputs.