Graph Convolutional Network（GCN）微信公众号文章
Graph Convolutional Networks | Thomas Kipf | PhD Student @ University of Amsterdam
https://tkipf.github.io/graph-convolutional-networks/#fn3
图卷积网络(Graph Convolutional networks, GCN) 简述
https://zhuanlan.zhihu.com/p/38612863
Graph Convolutional Networks (GCNs) 简介 - AHU-WangXiao - 博客园
https://www.cnblogs.com/wangxiaocvpr/p/8059769.html
如何理解 Graph Convolutional Network（GCN）？ - 知乎
https://www.zhihu.com/question/54504471
卷积神经网络不能处理“图”结构数据？这篇文章告诉你答案 | 雷锋网
https://www.leiphone.com/news/201706/ppA1Hr0M0fLqm7OP.html
深度学习在graph上的使用
https://zhuanlan.zhihu.com/p/27216346
专知 洛桑理工：Graph上的深度学习报告（附PPT下载）
https://mp.weixin.qq.com/s/Jt6CjMqNFEXWoL5pkLeVyw
新智元 Graph 卷积神经网络：概述、样例及最新进展
https://mp.weixin.qq.com/s/ZsnuY2ffUPbmCbBG-MnSyA
香港中大-商汤科技联合实验室AAAI录用论文详解：ST-GCN时空图卷积网络模型 https://mp.weixin.qq.com/s/GEbEDI-VDHPxCDW9SO7jtA
如何理解 Graph Convolutional Network（GCN）？ - 知乎
https://www.zhihu.com/question/54504471
AAAI 2018 | 时空图卷积网络：港中文提出基于动态骨骼的行为识别新方案
https://mp.weixin.qq.com/s/uxawHWsVXMNOTLNthAL0vg
实现
tkipf/gcn: Implementation of Graph Convolutional Networks in TensorFlow
https://github.com/tkipf/gcn
yysijie/st-gcn: Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch
https://github.com/yysijie/st-gcn
浅析图卷积神经网络 - 简书
https://www.jianshu.com/p/89fbed65cd04
《Graph Learning》| 图传播算法（上） - 简书
https://www.jianshu.com/p/53b4a3584199
《Graph learning》| 图传播算法（下） - 简书
https://www.jianshu.com/p/e7fb897b1d09
Advances in Deep Learning on Graphs
链接：https://pan.baidu.com/s/1mTqZQY85Oi0sW2jR7mohMA
密码：yfku
Semi-Supervised Classification with Graph Convolutional Networks
https://openreview.net/pdf?id=SJU4ayYgl
Modeling Relational Data with Graph Convolutional Networks
https://arxiv.org/abs/1703.06103
Inductive Representation Learning on Large Graphs
https://arxiv.org/abs/1706.02216
[1801.07606] Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
https://arxiv.org/abs/1801.07606
Eilene/spatio-temporal-paper-list: Spatio-temporal modeling 论文列表（主要是graph convolution相关)
https://github.com/Eilene/spatio-temporal-paper-list
How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)
https://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/
