Must-read papers on graph neural networks (GNN)

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

RuVector is a High Performance, Real-Time, Self-Learning, Vector Graph Neural Network, and Database built in Rust.

《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).

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

Programmable CUDA/C++ GPU Graph Analytics

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).

Transform geospatial relations into graphs for Graph Neural Networks and network analysis

The PyTorch implementation of STGCN.

An improved and reproducible implementation of a Silver Medal Kaggle NeurIPS Open Polymer Prediction solution, featuring SMILES canonicalization, molecular descriptors, CatBoost/XGBoost models, OOF stacking, and optional PyTorch Geometric GNNs.

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 (TNNLS 2023)

Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x

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

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).