各种同质图神经网络模型的理论和节点表征学习任务的集合包rgb_experiment

诸神缄默不语-个人优快云博文目录

本文仅考虑同质图setting下的节点表征模型。
对于异质图场景，可以参考我写的另一篇博文：异质图神经网络（持续更新ing…）

1. node2vec
2. ChebNet Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
3. GCN
4. GraphSAGE
5. (2018 ICLR) GAT Graph Attention Networks
   图注意力网络
6. (2018 ICLR) G2G Re37：读论文 G2G Graph2Gauss Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rank
7. (2018 ICML) JK-Net Representation Learning on Graphs with Jumping Knowledge Networks
8. GIN How Powerful are Graph Neural Networks?
9. GGNN
10. (2019 ICML) SGC Simplifying Graph Convolutional Networks
11. (2019 ICLR) APPNP Re0：读论文 PPNP/APPNP Predict then Propagate: Graph Neural Networks meet Personalized PageRank
12. (2019 KDD) PGE Re3：读论文 PGE A Representation Learning Framework for Property Graphs
13. (2020 KDD) DAGNN Re46：读论文 DAGNN Towards Deeper Graph Neural Networks
14. (2020 ICLR) CS-GNN Re2: 读论文 CS-GNN Measuring and Improving the Use of Graph Information in Graph Neural Networks
15. (2021 ICLR) C&S Re1：读论文 C&S (Correct and Smooth) Combining Label Propagation and Simple Models Out-performs Graph Ne
16. GAE
17. MotifNet: a motif-based Graph Convolutional Network for directed graphs
18. SIGN: Scalable Inception Graph Neural Networks
19. SSGC或S${}^2$GC Simple Spectral Graph Convolution
20. GBP Scalable Graph Neural Networks via Bidirectional Propagation
21. 无监督inductive节点表征模型（具体的还没看懂，所以不知道应该怎么分类啊啥的……）G2G Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
    Re37：读论文 G2G Graph2Gauss Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rank