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!
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Self-supervised Learning: Generative or Contrastive
X. Liu, F. Zhang, Z. Hou, Z. Wang, L. Mian, J. Zhang, and J. Tang
arXiv 2020 [PDF]
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Self-Supervised Learning of Graph Neural Networks: A Unified Review
Y. Xie, Z. Xu, Z. Wang, and S. Ji
arXiv 2021 [PDF]
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Graph Self-Supervised Learning: A Survey
Y. Liu, S. Pan, M. Jin, C. Zhou, F. Xia, and P. S. Yu
arXiv 2021 [PDF]
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Self-supervised on Graphs: Contrastive, Generative, or Predictive
L. Wu, H. Lin, Z. Gao, C. Tan, and S. Z. Li
arXiv 2021 [PDF]
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Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes
K. Sun, Z. Lin, and Z. Zhu
▷Node representation learning
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Strategies for Pre-training Graph Neural Networks
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec
▷Pretraining graphs
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When Does Self-Supervision Help Graph Convolutional Networks?
Y. You, T. Chen, Z. Wang, and Y. Shen
▷Node representation learning
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GPT-GNN: Generative Pre-Training of Graph Neural Networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun
▷Pretraining graphs
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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
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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
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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
▷Node representation learning
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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
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Bipartite Graph Embedding via Mutual Information Maximization
J. Cao, X. Lin, S. Guo, L. Liu, T. Liu, and B. Wang
▷Bipartite graph representation learning
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Contrastive Self-supervised Learning for Graph Classification
J. Zeng and P. Xie
AAAI 2021 [PDF]
▷Graph representation learning
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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
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Graph Contrastive Learning with Adaptive Augmentation
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang
▷Node representation learning
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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
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HDMI: High-order Deep Multiplex Infomax
B. Jing, C. Park, and H. Tong
WWW 2021 [PDF]
▷Multiplex graph representation learning
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Self-Supervised Graph Neural Networks Without Explicit Negative Sampling
Z. T. Kefato and S. Girdzijauskas
SSL@WWW 2021 [PDF]
▷Node representation learning
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Motif-Driven Contrastive Learning of Graph Representations
S. Zhang, Z. Hu, A. Subramonian, and Y. Sun
SSL@WWW 2021 [PDF]
▷Pretraining graphs
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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
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Towards Robust Graph Contrastive Learning
N. Jovanović, Z. Meng, L. Faber, and R. Wattenhofer
SSL@WWW 2021 [PDF]
▷Node representation learning
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Contrastive Learning with Hard Negative Samples
J. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka
ICLR 2021 [PDF]
▷Graph representation learning
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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
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Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs
C. Mavromatis and G. Karypis
PAKDD 2021 [PDF]
▷Node representation learning
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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
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Graph Contrastive Learning Automated
Y. You, T. Chen, Y. Shen, and Z. Wang
▷Graph representation learning
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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
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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
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Adversarial Graph Augmentation to Improve Graph Contrastive Learning,
S. Suresh, P. Li, C. Hao, and J. Neville
arXiv 2021 [PDF]
▷Graph representation learning
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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
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Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
W. Zhuo and G. Tan
arXiv 2021 [PDF]
▷Node representation learning
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Fairness-Aware Node Representation Learning
Ö. D. Köse and Y. Shen
arXiv 2021 [PDF]
▷Node representation learning
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Unsupervised Attributed Multiplex Network Embedding
C. Park, D. Kim, J. Han, and H. Yu
AAAI 2020 [PDF]
▷Multiplex graph representation learning
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Graph Representation Learning via Graphical Mutual Information Maximization
Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang
▷Node representation learning
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Contrastive Learning of Structured World Models
T. N. Kipf, E. van der Pol, and M. Welling
▷Relational inference
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Contrastive Multi-View Representation Learning on Graphs
K. Hassani and A. H. Khasahmadi
▷Node/graph representation learning
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Deep Graph Contrastive Representation Learning
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang
▷Node representation learning
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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
▷Pretraining graphs
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Graph Contrastive Learning with Augmentations
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen
▷Node representation learning, pretraining graphs
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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
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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
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Towards Domain-Agnostic Contrastive Learning
V. Verma, M.-T. Luong, K. Kawaguchi, H. Pham, and Q. V. Le
arXiv 2020 [PDF]
▷Graph representation learning
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Deep Graph Infomax
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm
▷Node representation learning
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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
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Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
X. Xia, H. Yin, J. Yu, Q. Wang, L. Cui, and X. Zhang
▷Session-based recommendation
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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
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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
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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
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Contrastive Self-Supervised Learning
Ankesh Anand
2020 [URL]
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Graph Contrastive Learning
Yanqiao Zhu
2021 [URL]
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Unsupervised Learning with Graph Neural Networks
Petar Veličković
ACDL 2019 Satellite Workshop on Graph Neural Networks [URL]