Homophily-Enhanced-Self-supervision

Graph Neural Network Library for PyTorch

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

PyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"

Anomaly detection related books, papers, videos, and toolboxes

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

links to conference publications in graph-based deep learning

A unified, comprehensive and efficient recommendation library

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