PyGCL: A PyTorch Library for Graph Contrastive Learning
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Updated
Dec 26, 2023 - Python
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"
Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.
Ratioanle-aware Graph Contrastive Learning codebase
Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
[SIGIR 2022] A Review-aware Graph Contrastive Learning Framework for Recommendation
ACM MM 2023 (Oral): Entropy neural estimation for graph contrastive learning
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”
[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)
GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
Code for AAAI'24 paper "Rethinking Graph Masked Autoencoders through Alignment and Uniformity”.
✨ 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"
Momentum Graph Contrastive Learning
The repository contains a reading list for graph learning.
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