图(graph)神经网络学习(三)--开源代码汇总

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