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https://github.com/Billy1900/GNN-Learning-and-Integration

Awesome GNN Learning For beginners
https://github.com/Billy1900/GNN-Learning-and-Integration

batch-size gnn gnn-learning graph-convolution graph-neural-networks machine-learning pytorch

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Awesome GNN Learning For beginners

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# GNN-Learning-and-Integration

First, for beginners, I really recommend they should start from a college course, like [CS224W: Machine Learning with Graphs, stanford Fall 2019](http://web.stanford.edu/class/cs224w/) and [Note](https://snap-stanford.github.io/cs224w-notes/) which could help you get a good understanding of networks and how we discover information out of it.

## 1. GNN Intuitive Learning

For those who do not know graph theory, this video [Fundamental graph theory](https://www.bilibili.com/video/av62661713/?spm_id_from=333.788.videocard.3) could help you to get a quick overview of graph theory. Convolution is an important part of GNN, and is kind of similar to CNN ([CNN explainer](https://github.com/poloclub/cnn-explainer) will help you get a visual understanding how convolution works.). However, it is still a little different, this post ([what is Convolution, graph Laplacian?](https://zhuanlan.zhihu.com/p/54505069)) will take you to grasp a deeper understading of graph convolution on the mathematical level. After that, there are some excellent works which try to give generalized explanations of GNN models such as [GNN model explainer](https://github.com/RexYing/gnn-model-explainer).



It is still not bad to start from some early classic works. For simplicity, I recommend one work which could help you open your eyes to GNN--GCN by kipf and the reading lists are as follows:

- [Graph Neural Network by kipf](http://tkipf.github.io/graph-convolutional-networks/)

- [GCN Introduction](https://zhuanlan.zhihu.com/p/120311352)

- [GCN 为什么是低通滤波器](https://zhuanlan.zhihu.com/p/142640571)

- 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)](https://www.cnblogs.com/SivilTaram/p/graph_neural_network_1.html)

  - [从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (二)](https://www.cnblogs.com/SivilTaram/p/graph_neural_network_2.html)

  - [从图(Graph)到图卷积(Graph Convolution)：漫谈图神经网络模型 (三)](https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html)



## 2. GNN Mathematical Theory Learning

For those who want get a deeper view of GNN math theory, I think those posts are very good and easy to understand.

- [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)



## 3. Academic Paper

### 3.1 Survey Paper

- [Graph Neural Networks-A Review of Methods and Applications.pdf](https://arxiv.org/abs/1812.08434)

- [Deep Learning on Graphs: A Survey](https://arxiv.org/pdf/1812.04202.pdf)

- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596)

- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://arxiv.org/pdf/2005.03675.pdf)

- [A Practical Guide to Graph Neural Networks](https://arxiv.org/abs/2010.05234)

- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://arxiv.org/abs/1903.03894)

### 3.2 Some Important Papers on GNN

- The graph neural network model

  - [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 (GCN)

  - [Semi Supervised Classification With Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)

  - [GCN code explanation](https://blog.csdn.net/yyl424525/article/details/100634211)

- [Variational Graph Auto-Encoders.pdf](https://arxiv.org/abs/1611.07308)



## 4. Curated list

- [Must read paper in GNN](https://github.com/Billy1900/GNN-Learning-and-Integration/blob/master/Must_read_paper_GNN.md)

- [Awesome graph neural networks paper list](https://github.com/nnzhan/Awesome-Graph-Neural-Networks)



## 5. Tools

### 5.1 Three Tools

Actually, I really recommend to use Keras in tensorflow (not pure tensorflow) and Pytorch bacause the two do not have too many version issues and have nice code styles.

  - Tensorflow

    - [How to Install Tensorflow 2.1.0 in windows10?： CUDA 10.1, CUDnn 7.6](https://blog.csdn.net/weixin_44170512/article/details/103990592)

    - [Medium--Migrating tensorflow 1.x to tensorflow 2.x.](https://medium.com/tensorflow/upgrading-your-code-to-tensorflow-2-0-f72c3a4d83b5)

    - [TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)](https://github.com/aymericdamien/TensorFlow-Examples)

  - Keras

  - Pytorch

    - [Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list): A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc

    - [How to solve problem: No module named torch_sparse](https://zhuanlan.zhihu.com/p/163180187)

### 5.2 Dataset

  - [Dataset library](https://linqs.soe.ucsc.edu/data)

  - [Cora Introduction](https://blog.csdn.net/yeziand01/article/details/93374216)

  - [Stanford Large Network Dataset Collection](https://snap.stanford.edu/data/)

  - [planetoid](https://github.com/kimiyoung/planetoid)

### 5.3 Library to build GNN easily

- [Deep Graph Library (DGL)](https://github.com/dmlc/dgl)

- [DIG](https://github.com/divelab/DIG): Dive into Graphs is a turnkey library for graph deep learning research.

- [Open Graph Benchmark](https://ogb.stanford.edu/): The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. 

- [GraphGym](http://snap.stanford.edu/gnn-design/): a powerful code platform for the community to explore GNN designs and tasks.

- [Graph Neural Networks with Keras and Tensorflow 2.](https://github.com/danielegrattarola/spektral): Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs).

- [CogDL: An Extensive Research Toolkit for Graphs](https://github.com/THUDM/cogdl/)

- [Graph Convolutional Neural Networks (GCNN) models](https://github.com/google/gcnn-survey-paper): This repository contains a tensorflow implementation of GCNN models for node classification, link predicition and joint node classification and link prediction to supplement the [survey paper by Chami et al.](https://arxiv.org/pdf/2005.03675.pdf)

- Benchmarking Graph Neural Networks [[paper]](https://arxiv.org/pdf/2003.00982v3.pdf) [[code]](https://github.com/graphdeeplearning/benchmarking-gnns)



### 5.4 Plotting

- Matplotlib教程 [[Link]](https://morvanzhou.github.io/tutorials/data-manipulation/plt/)

- [How to use t-SNE efficiently](https://distill.pub/2016/misread-tsne/)

- [Scikit-plot](https://github.com/reiinakano/scikit-plot)

- [Tools-to-Design-or-Visualize-Architecture-of-Neural-Network](https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network)

- ML Visuals [[repo]](https://github.com/dair-ai/ml-visuals) [[slides]](https://docs.google.com/presentation/d/11mR1nkIR9fbHegFkcFq8z9oDQ5sjv8E3JJp1LfLGKuk/edit#slide=id.g85a0789696_743_21)

- [Science Plots](https://github.com/garrettj403/SciencePlots)

- [Visualize of loss function](https://izmailovpavel.github.io/curves_blogpost/)



## 6. Courses & Learning material

- [吴恩达机器学习系列](https://zhuanlan.zhihu.com/p/108243142)

- [AlphaTree-graphic-deep-neural-network](https://github.com/weslynn/AlphaTree-graphic-deep-neural-network): 机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点

- [Awesome Math](https://github.com/llSourcell/learn_math_fast): A curated list of awesome mathematics resources.

- [Graph Representation Learning Book](https://www.cs.mcgill.ca/~wlh/grl_book/)

- [Deep Learning on Graphs](https://cse.msu.edu/~mayao4/dlg_book/)

- [Virgilio Data Science](https://github.com/virgili0/Virgilio)

- [C5.4 Networks From Harvard](https://courses.maths.ox.ac.uk/node/view_material/47273)



## 7. Graph Adversarial Learning

- [Awesome Graph Adversarial Learning (Updating)](https://github.com/gitgiter/Graph-Adversarial-Learning)

- [Awesome Graph Attack and Defense Papers](https://github.com/Billy1900/GCN-DP/blob/master/Awesome%20Graph%20Attack%20and%20Defense%20Papers.md)

- [DeepRobust: a repository with representative algorithms on Graph attack and defense model](https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph)

- [A curated list of adversarial attacks and defenses papers on graph-structured data](https://github.com/safe-graph/graph-adversarial-learning-literature)

- [图对抗攻击 Graph Adversarial Attack--zhihu](https://zhuanlan.zhihu.com/p/88934914)