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PyTorch implementation of Blockwise Graph Contrastive Learning (BlockGCL)
Code implementation of paper "Cluster-Perceptive Graph Contrastive Learning for Community Detection", ICASSP2025
The source code for "Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning"
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The code for "Deep Contrastive Graph Learning with Clustering-Oriented Guidance" (AAAI24).
[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"
XSGCL: A Lightweight Graph Contrastive Learning Framework for Recommendation
The official repo of A Universal Prompt-Tuning Framework for Graph Contrastive Learning-Based Recommendations (ACML2024 & Machine Learning Journal)
[TKDE25] Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
[AAAI 2024] Official repository for "Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective"
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning? (AAAI 2025)
The repository is an official PyTorch-based implementation for AAAI-25 paper “LOHA: Direct Graph Spectral \\Contrastive Learning between Low-pass and High-pass Views”
AAAI2025-Revisiting Graph Contrastive Learning on Anomaly Detection from a Structural Imbalance Perspective
The official source code for "Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling".
AAAI 2025, Beyond Homophily: Graph Contrastive Learning with Macro-Micro Message Passing
PyTorch Implementation for "Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning" (AAAI2023)
PyGCL: A PyTorch Library for Graph Contrastive Learning
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Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation (TCSVT 2024)
Codes for SimSTC published in EMNLP 2022.
PyTorch implementation of Contrastive Learning methods
Graph Neural Network Library for PyTorch
Open Source Code for GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models (KDD'24)
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Self-supervised semi-supervised nonnegative matrix factorization for data clustering