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).

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).

RuVector is a high performance vector and graph database built in Rust for AI, agentic systems, and real time analytics. It combines HNSW search, dynamic minimum cut coherence, graph intelligence, and self learning memory into one unified engine for scalable, low latency reasoning and structured retrieval.

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

The PyTorch implementation of STGCN.

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

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) 🍇

Filter the R-group and suspicious patterns in the polymer notation, use RDKit for parsability verification and unified conversion on this basis, add multi-source external data, grouping and mean aggregation with cS as the key to de-weight, multi-view GNN, CatBoost, XGboost modelling, using linear weighting for model fusion

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).