图相关的论文：GNN_Papers
一些开源的图(graph)模型
【1】Model_1: ChebNet(2016)-github-cnn_graph (tensorflow)
cnn到任意图的推广
{Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering}具有快速局部光谱滤波的图卷上的卷积神经网络
【2】Model_2: 1stChebNet(2017)-github-gcn (tensorflow)
{Semi-Supervised Classification with Graph Convolutional Networks}基于图卷积网络的半监督分类
【3】Model_3: GraphSage(2017)-github-GraphSAGE (tensorflow)
{GraphSage: Representation Learning on Large Graphs}GraphSage：大图上的表示学习
【4】Model_4: LGCN(2018)-github-lgcn (tensorflow)
{Large-Scale Learnable Graph Convolutional Networks(LGCN)}大规模可学习图形卷积网络（LGCN）
【5】Model_5: SplineCNN(2018)-github-pytorch_geometric (pytorch)
【6】Model_6: GAT(2017)-github-GAT (tensorflow)
【7】Model_7: GAE(2016)-github-Variational-Graph-Auto-Encoders (tensorflow)
【8】Model_8: ARGA(2018)-github-ARGA (tensorflow)
{Adversarially Regularized Graph Autoencoder (ARGA)}
【9】Model_9: SDNE(2016)-github-SDNE (python)
【10】Model_10: DRNE(2016)-github-DRNE (tensorflow)
【11】Model_11: GraphRNN(2018)-githu-GraphRNN (tensorflow)
【12】Model_12: DCRNN(2018)-github-DCRNN (tensorflow)
【13】Model_13: CNN-GCN(2017)-github-STGCN_IJCAI-18 (tensorflow)
【14】Model_14: ST-GCN(2018)-github-st-gcn (pytorch)
【15】 Heterogeneous Graph Attention Network (WWW-2019) 异质图分析
github：https://github.com/Jhy1993/HAN
阅读文章：https://blog.csdn.net/c9yv2cf9i06k2a9e/article/details/89484311
