> For the complete documentation index, see [llms.txt](https://luweikxy.gitbook.io/machine-learning-notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://luweikxy.gitbook.io/machine-learning-notes/graph-neural-networks/graph-convolutional-networks.md).

# GCN图卷积网络

* [返回顶层目录](https://luweikxy.gitbook.io/machine-learning-notes/graph-neural-networks/pages/-LpO5vE88qYwjk5WM_Qf#目录)
* [返回上层目录](/machine-learning-notes/graph-neural-networks.md)
* [GCN图卷积网络初步理解](https://github.com/luweikxy/machine-learning-notes/tree/7bb9e2dc5187381e19d9cf046511d67b61949496/content/deep-learning/graph-neural-networks/graph-convolutional-networks/gcn-preliminary-understand/gcn-preliminary-understand.md)
* [GCN图卷积网络的numpy简单实现](https://github.com/luweikxy/machine-learning-notes/tree/7bb9e2dc5187381e19d9cf046511d67b61949496/content/deep-learning/graph-neural-networks/graph-convolutional-networks/gcn-numpy-fulfillment/gcn-numpy-fulfillment.md)
* [GCN图卷积网络本质理解](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/gcn-essential-understand.md)
* [GCN图卷积网络全面理解](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/gcn-comprehensive-understand.md)
* [SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS ICLR2017](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/semi-supervised-classification-with-graph-convolutional-networks.md)
* [Graph Convolutional Neural Networks for Web-Scale Recommender Systems KDD2018](https://github.com/luweikxy/machine-learning-notes/tree/7bb9e2dc5187381e19d9cf046511d67b61949496/content/deep-learning/graph-neural-networks/graph-convolutional-networks/Graph-Convolutional-Neural-Networks-for-Web-Scale-Recommender-Systems.md)

\===

[知乎：如何理解 Graph Convolutional Network（GCN）？](https://www.zhihu.com/question/54504471)

[何时能懂你的心——图卷积神经网络（GCN）](https://mp.weixin.qq.com/s/I3MsVSR0SNIKe-a9WRhGPQ)

[Graph Neural Network：GCN 算法原理，实现和应用](https://mp.weixin.qq.com/s/ftz8E5LffWFfaSuF9uKqZQ)

[从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (一)](https://www.cnblogs.com/SivilTaram/p/graph_neural_network_1.html)

[如何理解 Graph Convolutional Network（GCN）？](https://ai.yanxishe.com/page/postDetail/13980?from=timeline)

[入门学习 | 什么是图卷积网络？行为识别领域新星](https://mp.weixin.qq.com/s/5wSgC4pXBfRLoCX-73DLnw)

[图卷积神经网络(GCN)详解:包括了数学基础(傅里叶，拉普拉斯)](https://zhuanlan.zhihu.com/p/67522582)

[论文浅尝 | 图神经网络综述：方法及应用](http://blog.openkg.cn/%E8%AE%BA%E6%96%87%E6%B5%85%E5%B0%9D-%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%BB%BC%E8%BF%B0%EF%BC%9A%E6%96%B9%E6%B3%95%E5%8F%8A%E5%BA%94%E7%94%A8/)

[图卷积神经网络相关资源：附有基于图卷积神经网络的实现、示例和教程；](https://github.com/Jiakui/awesome-gcn)

[20190820近期必读的7篇 IJCAI 2019【图神经网络（GNN）】相关论文](https://mp.weixin.qq.com/s/Mp-iLuPScFjyhq3IwzRGHA)

[20190806近期必读的12篇KDD 2019【图神经网络（GNN）】相关论文](https://mp.weixin.qq.com/s/r1K2Ry_GR1RN0frcr_HzLA)

[【清华NLP】图神经网络GNN论文分门别类，16大应用200+篇论文最新推荐](https://mp.weixin.qq.com/s/NYoObFBacOamjo2KHjJOAg)

[斯坦福大学最新论文|知识图卷积神经网络在推荐系统中的应用](https://mp.weixin.qq.com/s/4KS_HG7rBOQgcTII7YKsaQ)

论文：《SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation》（AAAI2019）

<https://zhuanlan.zhihu.com/p/73679727>

菜鸟笔记之《Large-Scale Learnable Graph Convolutional Networks》

<https://www.jianshu.com/p/ada8730913ce>

[Google图嵌入工业界最新大招，高效解决训练大规模深度图卷积神经网络问题](https://mp.weixin.qq.com/s?__biz=MzU2ODA0NTUyOQ==\&mid=2247483775\&idx=1\&sn=735e671a4223e47149197c5eacd94e0a\&chksm=fc92bbc9cbe532df3742dbad41c7364d536a6e56dd18030a41fadd7ad409a7e8fb02ac027bc6\&scene=21#wechat_redirect)

[ICCV 2019 | 成功从3/4层拓展到56层，训练超级深层的图卷积神经网络,tf](https://zhuanlan.zhihu.com/p/86352650)


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