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.

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

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)