add papers

README.md

@@ -7,3 +7,41 @@ I use basic packages from Anaconda3 with Python 3.8.5. To make my life easier, I
 torch==1.7.0
 torch_geometric==1.6.3
 ```
+# 3. Papers
+The following are papers that I'll cover in this repo. 
+## 3.1 Early Research
+### 3.1.1 Factorization-Based Models
+- **Distributed Large-scale Natural Graph Factorization.**
+*Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola.*
+   WWW 2013.
+
+- **Grarep: Learning graph representations with global structural information.**
+*Shaosheng Cao, Wei Lu, and Qiongkai Xu.*
+   CIKM 2015.
+
+- **Asymmetric Transitivity Preserving Graph Embedding**
+*Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu.*
+  KDD 2016.
+### 3.1.2 Random Walk-Based Models
+
+## 3.2 Scalability and Expressivity
+### 3.2.1 Node Sampling
+### 3.2.2 Subgraph Sampling
+### 3.2.3 Regularization
+### 3.2.4 Architecture
+
+## 3.3 Incorporating Edge and Label Information
+### 3.3.1 Incorporating Edge Information
+### 3.3.2 Incorporating Label Information
+
+## 3.4 Training Strategy
+
+## 3.5 Generalization to Heterogeneous Graphs
+### 3.5.1 Random Walk-Based Models
+### 3.5.2 GCN-Based Models
+
+### 3.5.3 Application
+## 3.6 Interpretability and Theory Guidance
+### 3.6.1 Expressive Power of GCNs
+### 3.6.2 When Will GCNs Fail
+### 3.6.3 How to Design Better GCNs