A comprehensive (masked) graph autoencoders benchmark.
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Updated
Oct 17, 2024 - Python
A comprehensive (masked) graph autoencoders benchmark.
PyGCL: A PyTorch Library for Graph Contrastive Learning
[KDD 2024] Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Code for AAAI'24 paper "Rethinking Graph Masked Autoencoders through Alignment and Uniformity”.
GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)
[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"
The source code of "Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Official code for TNNLS paper "Affinity Uncertainty-based Hard Negative Mining in 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.
Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
This is the code repo for Violin, an IJCAI 2023 paper.
✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
ACM MM 2023 (Oral): Entropy neural estimation for graph contrastive learning
Ratioanle-aware Graph Contrastive Learning codebase
[ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"
Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation"
Momentum Graph Contrastive Learning
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