/Awesome-Graph-Contrastive-Learning

A collection of graph contrastive learning methods.

Awesome Graph Contrastive Learning

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A collection of Graph Contrastive Learning methods.

It's worth noting that many traditional unsupervised graph representation learning methods inherently follow the contrastive paradigm (e.g., DeepWalk, node2vec, GAE/VGAE etc.). We will not elaborate on them here.

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Paper references with implementation

Empirical Study

2021

  • An Empirical Study of Graph Contrastive Learning (OpenReview)

    • Authors: Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu
    • [paper]
    • [code]

  • Evaluating Modules in Graph Contrastive Learning (arXiv)

    • Authors: Ganqu Cui, Yufeng Du, Cheng Yang, Jie Zhou, Liang Xu, Lifeng Wang, Zhiyuan Liu
    • [paper]
    • [code]

InfoMax Principle

2021

  • Bipartite Graph Embedding via Mutual Information Maximization (WSDM)

    • Authors: Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang
    • [paper]
    • [code]

  • SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism (WWW)

  • Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization (arXiv)

    • Authors: Chenguang Wang, Ziwen Liu
    • [paper]

  • Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations (arXiv)

    • Authors: Xiaolong Fan, Maoguo Gong, Yue Wu, Hao Li
    • [paper]

2020

  • Contrastive Multi-View Representation Learning on Graphs (ICML)

  • Graph Representation Learning via Graphical Mutual Information Maximization (WWW)

    • Authors: Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang
    • [paper]
    • [code]

  • InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (ICLR)

    • Authors: Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang
    • [paper]
    • [code]

  • CommDGI: Community Detection Oriented Deep Graph Infomax (CIKM)

    • Authors: Tianqi Zhang, Yun Xiong, Jiawei Zhang, Yao Zhang, Yizhu Jiao, Yangyong Zhu
    • [paper]

  • Exploiting Mutual Information for Substructure-aware Graph Representation Learning (IJCAI)

    • Authors: Pengyang Wang, Yanjie Fu, Yuanchun Zhou, Kunpeng Liu, Xiaolin Li, Kien Hua
    • [paper]

  • Attributed Network Embedding based on Mutual Information Estimation (CIKM)

    • Authors: Xiaomin Liang, Daifeng Li, Andrew Madden
    • [paper]
    • [code]

  • Graph Contrastive Learning with Local and Global Mutual Information Maximization (ICIT)

    • Authors: Yifei Hu, Ya Zhang
    • [paper]

  • Unsupervised Attributed Multiplex Network Embedding (AAAI)

    • Authors: Chanyoung Park, Donghyun Kim, Jiawei Han, Hwanjo Yu
    • [paper]
    • [code]

2019

  • Deep Graph InfoMax (ICLR)

    • Authors: Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm
    • [paper]
    • [code]

  • Spatio-Temporal Deep Graph InfoMax (ICLR workshp @ RLGM)

    • Authors: Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R Devon Hjelm
    • [paper]

GNN Pre-Training

2020

  • GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (KDD)

    • Authors: Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
    • [paper]
    • [code]

  • Graph Contrastive Learning with Augmentations (NeurIPS)

    • Authors: Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
    • [paper]
    • [code]

Graph Augmentation

2022

  • Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices (WWW)

    • Authors: Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
    • [paper]

  • AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators (AAAI)

    • Authors: Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang
    • [paper]
    • [code]

2021

  • Graph Contrastive Learning with Adaptive Augmentation (WWW)

    • Authors: Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
    • [paper]
    • [code]

  • Graph Contrastive Learning Automated (ICML)

    • Authors: Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang
    • [paper]
    • [code]

  • Adversarial Graph Augmentation to Improve Graph Contrastive Learning (NIPS)

    • Authors: Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville
    • [paper]
    • [code]

  • Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast (arXiv)

    • Authors: Wei Zhuo, Guang Tan
    • [paper]

  • Jointly Learnable Data Augmentations for Self-Supervised GNNs (arXiv)

    • Authors: Zekarias T. Kefato, Sarunas Girdzijauskas, Hannes Stärk
    • [paper]
    • [code]

Others

2021

  • Directed Graph Contrastive Learning (NIPS)

    • Authors: Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang
    • [paper]
    • [code]

  • InfoGCL: Information-Aware Graph Contrastive Learning (NIPS)

    • Authors: Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang
    • [paper]

  • Graph Adversarial Self-Supervised Learning (NIPS)

    • Authors: Longqi Yang, Liangliang Zhang, Wenjing Yang
    • [paper]
    • [code]

  • Motif-Driven Contrastive Learning of Graph Representations (arXiv)

    • Authors: Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun
    • [paper]

  • Fairness-Aware Node Representation Learning (arXiv)

    • Authors: Öykü Deniz Köse, Yanning Shen
    • [paper]

  • Towards Domain-Agnostic Contrastive Learning (ICML)

    • Authors: Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le
    • [paper]

  • Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction (WWW)

    • Authors: Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
    • [paper]

  • Contrastive Self-supervised Learning for Graph Classification (AAAI)

    • Authors: Jiaqi Zeng, Pengtao Xie
    • [paper]

2020

  • Deep Graph Contrastive Representation Learning (ICML Workshop on GRL)

    • Authors: Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
    • [paper]
    • [code]

  • Sub-graph Contrast for Scalable Self-Supervised Graph Representaion Learning (ICDM)

    • Authors: Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, Yangyong Zhu
    • [paper]
    • [code]