A curated list for awesome self-supervised graph representation learning resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, awesome-architecture-search, and awesome-self-supervised-learning.

Self-supervised learning is the future! — Yann LeCun

Recently self-supervised learning (SSL) techniques have gained success in many domains, e.g., visual, natural language processing, and robotics, where SSL methods even outperform their supervised counterparts. However, the development of SSL in the graph domain is still at a nascent stage. Can SSL graph representation achieve similar or even better performance than its supervised opponents? This repository provides you with a curated list of awesome self-supervised graph representation learning resources. Following [Ankesh Anand 2020], we roughly divide papers into two lines: generative/predictive (i.e. optimizing in the output space) and contrastive methods (i.e. optimizing in the latent space). Along with papers, we also list several must-read blog posts and talks.

Feel free to send pull requests to add more links!

• Papers
  • Surveys
  • Generative/Predictive Methods
  • Contrastive Methods
• Blog Posts
• Talks

• Self-supervised Learning: Generative or Contrastive

  X. Liu, F. Zhang, Z. Hou, Z. Wang, L. Mian, J. Zhang, and J. Tang

  arXiv 2020 [PDF]

• Self-Supervised Learning of Graph Neural Networks: A Unified Review

  Y. Xie, Z. Xu, Z. Wang, and S. Ji

  arXiv 2021 [PDF]

• Graph Self-Supervised Learning: A Survey

  Y. Liu, S. Pan, M. Jin, C. Zhou, F. Xia, and P. S. Yu

  arXiv 2021 [PDF]

• Self-supervised on Graphs: Contrastive, Generative, or Predictive

  L. Wu, H. Lin, Z. Gao, C. Tan, and S. Z. Li

  arXiv 2021 [PDF]

• Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes

  K. Sun, Z. Lin, and Z. Zhu

  AAAI 2020 [PDF, Code]

  ▷Node representation learning

• Strategies for Pre-training Graph Neural Networks

  W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec

  ICLR 2020 [PDF, Code]

  ▷Pretraining graphs

• When Does Self-Supervision Help Graph Convolutional Networks?

  Y. You, T. Chen, Z. Wang, and Y. Shen

  ICML 2020 [PDF, Code]

  ▷Node representation learning

• GPT-GNN: Generative Pre-Training of Graph Neural Networks

  Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun

  KDD 2020 [PDF, Code]

  ▷Pretraining graphs

• Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

  D. Hwang, J. Park, S. Kwon, K.-M. Kim, J.-W. Ha, and H. J. Kim

  NeurIPS 2020 [PDF]

  ▷Heterogeneous graphs

• CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning

  Y. Zhu, Y. Xu, F. Yu, S. Wu, and L. Wang

  arXiv 2020 [PDF]

  ▷Node representation learning

• Self-supervised Learning on Graphs: Deep Insights and New Direction

  W. Jin, T. Derr, H. Liu, Y. Wang, S. Wang, Z. Liu, and J. Tang

  arXiv 2020 [PDF, Code]

  ▷Node representation learning

• Self-Supervised Graph Representation Learning via Global Context Prediction

  Z. Peng, Y. Dong, M. Luo, X.-M. Wu, and Q. Zheng

  arXiv 2020 [PDF]

  ▷Node representation learning

• Bipartite Graph Embedding via Mutual Information Maximization

  J. Cao, X. Lin, S. Guo, L. Liu, T. Liu, and B. Wang

  WSDM 2021 [PDF, Code]

  ▷Bipartite graph representation learning

• Contrastive Self-supervised Learning for Graph Classification

  J. Zeng and P. Xie

  AAAI 2021 [PDF]

  ▷Graph representation learning

• Contrastive and Generative Graph Convolutional Networks for Graph-Based Semi-Supervised Learning

  S. Wan, S. Pan, J. Yang, and C. Gong

  AAAI 2021 [PDF]

  ▷Semi-supervised node representation learning

• Graph Contrastive Learning with Adaptive Augmentation

  Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang

  WWW 2021 [PDF, Code]

  ▷Node representation learning

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

  Q. Sun, J. Li, H. Peng, J. Wu, Y. Ning, P. S. Yu, and L. He

  WWW 2021 [PDF]

  ▷Graph representation learning

• HDMI: High-order Deep Multiplex Infomax

  B. Jing, C. Park, and H. Tong

  WWW 2021 [PDF]

  ▷Multiplex graph representation learning

• Self-Supervised Graph Neural Networks Without Explicit Negative Sampling

  Z. T. Kefato and S. Girdzijauskas

  SSL@WWW 2021 [PDF]

  ▷Node representation learning

• Motif-Driven Contrastive Learning of Graph Representations

  S. Zhang, Z. Hu, A. Subramonian, and Y. Sun

  SSL@WWW 2021 [PDF]

  ▷Pretraining graphs

• Iterative Graph Self-Distillation

  H. Zhang, S. Lin, W. Liu, P. Zhou, J. Tang, X. Liang, and E. P. Xing

  SSL@WWW 2021 [PDF]

  ▷Graph representation learning

• Towards Robust Graph Contrastive Learning

  N. Jovanović, Z. Meng, L. Faber, and R. Wattenhofer

  SSL@WWW 2021 [PDF]

  ▷Node representation learning

• Contrastive Learning with Hard Negative Samples

  J. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka

  ICLR 2021 [PDF]

  ▷Graph representation learning

• Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

  M. Jin, Y. Zheng, Y.-F. Li, C. Gong, C. Zhou, and S. Pan

  IJCAI 2021 [PDF]

  ▷Node representation learning

• Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs

  C. Mavromatis and G. Karypis

  PAKDD 2021 [PDF]

  ▷Node representation learning

• Self-supervised Graph-level Representation Learning with Local and Global Structure

  M. Xu, H. Wang, B. Ni, H. Guo, and J. Tang

  ICML 2021 [PDF]

  ▷Pretraining graphs

• Graph Contrastive Learning Automated

  Y. You, T. Chen, Y. Shen, and Z. Wang

  ICML 2021 [PDF, Code]

  ▷Graph representation learning

• Bootstrapped Representation Learning on Graphs

  S. Thakoor, C. Tallec, M. G. Azar, R. Munos, P. Veličković, and M. Valko

  arXiv 2021 [PDF]

  ▷Node representation learning

• Improving Graph Representation Learning by Contrastive Regularization

  K. Ma, H. Yang, H. Yang, T. Jin, P. Chen, Y. Chen, B. F. Kamhoua, and J. Cheng

  arXiv 2021 [PDF]

  ▷Graph/node representation learning

• Adversarial Graph Augmentation to Improve Graph Contrastive Learning,

  S. Suresh, P. Li, C. Hao, and J. Neville

  arXiv 2021 [PDF]

  ▷Graph representation learning

• Automated Self-Supervised Learning for Graphs

  W. Jin, X. Liu, X. Zhao, Y. Ma, N. Shah, and J. Tang

  arXiv 2021 [PDF]

  ▷Node representation learning

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

  W. Zhuo and G. Tan

  arXiv 2021 [PDF]

  ▷Node representation learning

• Fairness-Aware Node Representation Learning

  Ö. D. Köse and Y. Shen

  arXiv 2021 [PDF]

  ▷Node representation learning

• Unsupervised Attributed Multiplex Network Embedding

  C. Park, D. Kim, J. Han, and H. Yu

  AAAI 2020 [PDF]

  ▷Multiplex graph representation learning

• Graph Representation Learning via Graphical Mutual Information Maximization

  Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang

  WWW 2020 [PDF, Code]

  ▷Node representation learning

• Contrastive Learning of Structured World Models

  T. N. Kipf, E. van der Pol, and M. Welling

  ICLR 2020 [PDF, Code]

  ▷Relational inference

• Contrastive Multi-View Representation Learning on Graphs

  K. Hassani and A. H. Khasahmadi

  ICML 2020 [PDF, Code]

  ▷Node/graph representation learning

• Deep Graph Contrastive Representation Learning

  Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang

  GRL+@ICML 2020 [PDF, Code]

  ▷Node representation learning

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

  J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang

  KDD 2020 [PDF, Code]

  ▷Pretraining graphs

• Graph Contrastive Learning with Augmentations

  Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen

  NeurIPS 2020 [PDF, Code]

  ▷Node representation learning, pretraining graphs

• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

  Y. Jiao, Y. Xiong, J. Zhang, Y. Zhang, T. Zhang, and Y. Zhu

  ICDM 2020 [PDF]

  ▷Sub-graph representation learning

• Self-supervised Smoothing Graph Neural Networks

  L. Yu, S. Pei, C. Zhang, L. Ding, J. Zhou, L. Li, and X. Zhang

  arXiv 2020 [PDF]

  ▷Node representation learning

• Towards Domain-Agnostic Contrastive Learning

  V. Verma, M.-T. Luong, K. Kawaguchi, H. Pham, and Q. V. Le

  arXiv 2020 [PDF]

  ▷Graph representation learning

• Deep Graph Infomax

  P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm

  ICLR 2019 [PDF, Code]

  ▷Node representation learning

• Spatio-Temporal Deep Graph Infomax

  F. L. Opolka, A. Solomon, C. Cangea, P. Veličković, P. Liò, and R. D. Hjelm

  RLGM@ICLR 2019 [PDF]

  ▷Node representation learning

• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

  X. Xia, H. Yin, J. Yu, Q. Wang, L. Cui, and X. Zhang

  AAAI 2021 [PDF, Code]

  ▷Session-based recommendation

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

  Y. Wang, Y. Min, X. Chen, and J. Wu

  WWW 2021 [PDF]

  ▷Drug-drug interaction prediction

• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

  J. Yu, H. Yin, J. Li, Q. Wang, N. Q. V. Hung, and X. Zhang

  WWW 2021 [PDF]

  ▷Social recommendation

• Self-supervised Graph Learning for Recommendation

  J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie

  SIGIR 2021 [PDF]

  ▷Collaborative filtering

• Contrastive Self-Supervised Learning

  Ankesh Anand

  2020 [URL]

• Graph Contrastive Learning

  Yanqiao Zhu

  2021 [URL]

• Unsupervised Learning with Graph Neural Networks

  Petar Veličković

  ACDL 2019 Satellite Workshop on Graph Neural Networks [URL]