GNN 学习-从浅到深-从直观理解到数学推导

雨点打透心脏的1/2处 2023-05-21 15:29 18阅读 0赞

# GNN-Learning-and-Integration #

Sorry for that, most of the materials are written in Chinese. Friendly for beginners.

Code is easily to find on github.

## 1. GNN Intuitive Learning ##

*  [Fundamental graph theory][]
 *  [Deep Learning on Graph: GraphSAGE][Deep Learning on Graph_ GraphSAGE]
 *  [what is Convolution, graph Laplacian][what is Convolution_ graph Laplacian]
 *  [Graph Neural Network by kipf][]
 *  从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型
    
     *  [从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (一)][Graph_Graph Convolution_]
     *  [从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (二)][Graph_Graph Convolution_ 1]
     *  [从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (三)][Graph_Graph Convolution_ 2]

## 2. GNN Mathematical Theory Learning ##

*  [GNN Conclusions][]
 *  [GNN Review report][]
 *  [Graph model: graph embedding and graph convolutional network][Graph model_ graph embedding and graph convolutional network]
 *  [Mathematical foundation of GNN][]
 *  [Dive into Convolution deeply: Mathematical derivation][Dive into Convolution deeply_ Mathematical derivation]

## 3. Academic Paper ##

*  [Graph Neural Networks-A Review of Methods and Applications.pdf][]
 *  The graph neural network model
    
     *  [The graph neural network model][]
     *  [The Graph Neural Network Model explanation][]
 *  [Diffusion-Convolutional Neural Networks][]
 *  [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks][]
 *  [semi\_supervised\_classification\_with\_graph\_convolutional\_networks.pdf][semi_supervised_classification_with_graph_convolutional_networks.pdf]
 *  [Variational Graph Auto-Encoders.pdf][]

## 4. Survey ##

*  [Must read paper in GNN][]

## 5. Tools ##

*  [Three Tools][]
    
     *  Tensorflow
     *  Keras
     *  Pytorch
 *  [Dataset][]

## 6. Other ##

*  [Research Methods in Machine Learning][]

More material is on my github: https://github.com/Billy1900/GNN-Learning-and-Integration

[Fundamental graph theory]: https://www.bilibili.com/video/av62661713/?spm_id_from=333.788.videocard.3
[Deep Learning on Graph_ GraphSAGE]: https://www.bilibili.com/video/av51673220/?spm_id_from=333.788.videocard.1
[what is Convolution_ graph Laplacian]: https://zhuanlan.zhihu.com/p/54505069
[Graph Neural Network by kipf]: http://tkipf.github.io/graph-convolutional-networks/
[Graph_Graph Convolution_]: https://www.cnblogs.com/SivilTaram/p/graph_neural_network_1.html
[Graph_Graph Convolution_ 1]: https://www.cnblogs.com/SivilTaram/p/graph_neural_network_2.html
[Graph_Graph Convolution_ 2]: https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html
[GNN Conclusions]: https://zhuanlan.zhihu.com/p/76001080
[GNN Review report]: https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/GNN_Review1.1.pdf
[Graph model_ graph embedding and graph convolutional network]: https://zhuanlan.zhihu.com/p/65539782
[Mathematical foundation of GNN]: http://xtf615.com/2019/02/24/gcn/
[Dive into Convolution deeply_ Mathematical derivation]: https://www.zhihu.com/question/54504471/answer/332657604
[Graph Neural Networks-A Review of Methods and Applications.pdf]: https://arxiv.org/abs/1812.08434
[The graph neural network model]: http://persagen.com/files/misc/scarselli2009graph.pdf
[The Graph Neural Network Model explanation]: https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/%E8%AE%BA%E6%96%87%E3%80%8AThe%20Graph%20Neural%20Network%20Model%E3%80%8B%E4%B8%ADGNN%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%AE%9E%E7%8E%B0%E7%BB%86%E8%8A%82.pdf
[Diffusion-Convolutional Neural Networks]: https://papers.nips.cc/paper/6212-diffusion-convolutional-neural-networks.pdf
[Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks]: https://arxiv.org/abs/1503.00075
[semi_supervised_classification_with_graph_convolutional_networks.pdf]: https://arxiv.org/abs/1609.02907
[Variational Graph Auto-Encoders.pdf]: https://arxiv.org/abs/1611.07308
[Must read paper in GNN]: https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/Must_read_paper_GNN.md
[Three Tools]: https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%89%E5%A4%A7%E6%A1%86%E6%9E%B6%E5%AD%A6%E4%B9%A0.md
[Dataset]: https://linqs.soe.ucsc.edu/data
[Research Methods in Machine Learning]: https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/new-in-ml-2019.pdf