Must-read papers on graph neural networks (GNN)

《深入浅出图神经网络：GNN原理解析》配套代码

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

An Industrial Graph Neural Network Framework

Programmable CUDA/C++ GPU Graph Analytics

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

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

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

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

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

The PyTorch implementation of STGCN.

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

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

图神经网络整理

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.

Code and resources on scalable and efficient Graph Neural Networks

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

PyTorch implementation of Graph Matching Networks, e.g., Graph Matching with Bi-level Noisy Correspondence (COMMON, ICCV 2023), Graph Matching Networks for Learning the Similarity of Graph Structured Objects (GMN, ICML 2019).

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