Update README.md

README.md

@@ -6,6 +6,8 @@ This is a pytorch implementation of submission: <b>Multi-Relational Graph Attent
 
 <i>IJCAI'24</i>
 
+<div align=center><img src="https://github.com/qinguangming1999/MRGAN_IJCAI/blob/main/overview.png" width="900"/></div>
+
 ## abstract
 Inferring social relationships from human mobility data holds significant value in real-life spatio-temporal applications, inspiring the development of a series of graph-based methods for deriving such relationships.
 However, despite their noted effectiveness, we argue that previous methods either rely solely on direct relations between users, neglecting valuable user mobility patterns, or have not fully harnessed the indirect interactions, thereby struggling to capture users' mobility preferences. To address these issues, in this work, we propose the Multi-Relational Graph Attention Network MRGAN, a novel graph attention network, which is able to explicitly model indirect relations and effectively capture their different impact. Specifically, we first extract a multi-relational graph from heterogeneous mobility graph to explicitly model the direct and indirect relations, %as different mobility patterns
@@ -16,15 +18,18 @@ Comprehensive experiments on three real-world mobile datasets demonstrate that t
 
 ## Requirements
   * Python 3.8.6
-  * tensorflow 2.3.1
+  * torch 2.1.2
+  * pandas 2.3.1
+  * keras 2.11.0
   * networkx
   * numpy
   * scipy
+  * scikit-learn
+  * dgl 1.1.2
 
 ## Run the code
 
-
-**Python train_MRGAN.PY to run the code** 
+**Python train_MRGAN.py to run the code**