PyGCL: A PyTorch Library for Graph Contrastive Learning

[ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation"

[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"

An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.

Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning

Ratioanle-aware Graph Contrastive Learning codebase

Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily

[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)

GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)

[SIGIR 2022] A Review-aware Graph Contrastive Learning Framework for Recommendation

The source code of "Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks

Source code of NeurIPS 2022 paper “Co-Modality Graph Contrastive Learning for Imbalanced Node Classification”

ACM MM 2023 (Oral): Entropy neural estimation for graph contrastive learning

Code for AAAI'24 paper "Rethinking Graph Masked Autoencoders through Alignment and Uniformity”.

[KDD 2024] Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective

✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG

Official code for TNNLS paper "Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning"

A comprehensive (masked) graph autoencoders benchmark.