Must-read papers on graph neural networks (GNN)

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

《深入浅出图神经网络：GNN原理解析》配套代码

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

An Industrial Graph Neural Network Framework

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

Programmable CUDA/C++ GPU Graph Analytics

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

A repository of pretty cool datasets that I collected for network science and machine learning research.

图神经网络整理

Code and resources on scalable and efficient Graph Neural Networks

The PyTorch implementation of STGCN.

🍇 A C++ library for parallel graph processing (GRAPE) 🍇

A PyTorch Graph Neural Network Library

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.

[NeurIPS'22] Tokenized Graph Transformer (TokenGT), in PyTorch

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.