graph-neural-networks

Graph Neural Network Library for PyTorch

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!

links to conference publications in graph-based deep learning

A unified, comprehensive and efficient recommendation library

RuVector is a High Performance, Real-Time, Self-Learning Ai, Vector GNN, Memory DB built in Rust.

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

StellarGraph - Machine Learning on Graphs

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

A distributed graph deep learning framework.

Repository for benchmarking graph neural networks (JMLR 2023)

Graph Neural Networks with Keras and Tensorflow 2.

Benchmark datasets, data loaders, and evaluators for graph machine learning

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!

Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.