Papers about self-supervised learning on Graph Neural Networks (GNNs). If you feel there are papers with related topics missing, do not hesitate to let us know (via issues or pull requests).

1. [NeurIPS 2021] Directed Graph Contrastive Learning [paper][code]
2. [NeurIPS 2021] Multi-view Contrastive Graph Clustering [paper][code]
3. [NeurIPS 2021] From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [paper][code]
4. [NeurIPS 2021] InfoGCL: Information-Aware Graph Contrastive Learning [paper]
5. [NeurIPS 2021] Adversarial Graph Augmentation to Improve Graph Contrastive Learning [paper][code]
6. [NeurIPS 2021] Disentangled Contrastive Learning on Graphs [[coming soon]]
7. [CIKM 2021] Multimodal Graph Meta Contrastive Learning [paper]
8. [CIKM 2021] Self-supervised Representation Learning on Dynamic Graphs [paper]
9. [CIKM 2021] Rectifying Pseudo Labels: Iterative Feature Clustering for Graph Representation Learning [paper]
10. [CIKM 2021] SGCL: Contrastive Representation Learning for Signed Graphs [paper]
11. [CIKM 2021] Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks [paper]
12. [arXiv 2021] Graph Communal Contrastive Learning [paper]
13. [arXiv 2021] Self-supervised Contrastive Attributed Graph Clustering [paper]
14. [arXiv 2021] Self-Supervised Learning for Molecular Property Prediction [paper]
15. [arXiv 2021] RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training [paper]
16. [arXiv 2021] Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation [paper]
17. [arXiv 2021] PRE-TRAINING MOLECULAR GRAPH REPRESENTATION WITH 3D GEOMETRY [paper] [code]
18. [arXiv 2021] 3D Infomax improves GNNs for Molecular Property Prediction [paper] [code]
19. [CVPR 2021] Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs [paper]
20. [arXiv 2021] Motif-based Graph Self-Supervised Learning for Molecular Property Prediction [paper]
21. [arXiv 2021] Debiased Graph Contrastive Learning [paper]
22. [arXiv 2021] 3D-Transformer: Molecular Representation with Transformer in 3D Space [paper]
23. [arXiv 2021] Contrastive Pre-Training of GNNs on Heterogeneous Graphs [paper]
24. [arXiv 2021] Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores [paper]
25. [arXiv 2021] GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction [paper]
26. [arXiv 2021] Adaptive Multi-layer Contrastive Graph Neural Networks [paper]
27. [KBS 2021] Multi-aspect self-supervised learning for heterogeneous information network [paper]
28. [arXiv 2021] Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs [paper]
29. [arXiv 2021] Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [paper]
30. [arXiv 2021] Negative Sampling Strategies for Contrastive Self-Supervised Learning of Graph Representations [paper]
31. [arXiv 2021] Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning [paper]
32. [arXiv 2021] Spatio-Temporal Graph Contrastive Learning [paper]
33. [arXiv 2021] Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [paper]
34. [IJCAI 2021] Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning [paper]
35. [IJCAI 2021] Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks [paper]
36. [IJCAI 2021] CuCo: Graph Representation with Curriculum Contrastive Learning [paper]
37. [IJCAI 2021] Graph Debiased Contrastive Learning with Joint Representation Clustering [paper]
38. [IJCAI 2021] CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction [paper]
39. [arXiv 2021] GCCAD: Graph Contrastive Coding for Anomaly Detection [paper]
40. [arXiv 2021] Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [paper]
41. [arXiv 2021] RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks [paper]
42. [KDD 2021] MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph [paper] [code]
43. [KDD 2021] Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment [paper]
44. [KDD 2021] Adaptive Transfer Learning on Graph Neural Networks [paper]
45. [arXiv 2021] Group Contrastive Self-Supervised Learning on Graphs [paper]
46. [arXiv 2021] Multi-Level Graph Contrastive Learning [paper]
47. [Openreview 2021] An Empirical Study of Graph Contrastive Learning [paper]
48. [arXiv 2021] From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [paper] [code]
49. [arXiv 2021] Evaluating Modules in Graph Contrastive Learning [paper] [code]
50. [ICML 2021] Graph Contrastive Learning Automated [paper] [code]
51. [arXiv 2021] Automated Self-Supervised Learning for Graphs [paper] [code]
52. [ICML 2021] Self-supervised Graph-level Representation Learning with Local and Global Structure [paper] [code]
53. [KDD 2021] Pre-training on Large-Scale Heterogeneous Graph [paper]
54. [KDD 2021] MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge [paper]
55. [KDD 2021] Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning [paper] [code]
56. [arXiv 2021] Prototypical Graph Contrastive Learning [paper]
57. [arXiv 2021] Fairness-Aware Node Representation Learning [paper]
58. [arXiv 2021] Adversarial Graph Augmentation to Improve Graph Contrastive Learning [paper]
59. [arXiv 2021] Graph Barlow Twins: A self-supervised representation learning framework for graphs [paper]
60. [arXiv 2021] Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast [paper]
61. [arXiv 2021] Self-supervised on Graphs: Contrastive, Generative,or Predictive [paper]
62. [arXiv 2021] FedGL: Federated Graph Learning Framework with Global Self-Supervision [paper]
63. [IJCNN 2021] Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation [paper]
64. [arXiv 2021] Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks [paper]
65. [arXiv 2021] Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities [paper]
66. [arXiv 2021] Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization [paper]
67. [arXiv 2021] Drug Target Prediction Using Graph Representation Learning via Substructures Contrast [paper]
68. [arXiv 2021] Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning [paper]
69. [arXiv 2021] Graph Self-Supervised Learning: A Survey [paper]
70. [arXiv 2021] Towards Robust Graph Contrastive Learning [paper]
71. [arXiv 2021] Pre-Training on Dynamic Graph Neural Networks [paper]
72. [arXiv 2021] Self-Supervised Learning of Graph Neural Networks: A Unified Review [paper]
73. [WWW 2021 Workshop] Iterative Graph Self-Distillation [paper]
74. [WWW 2021] HDMI: High-order Deep Multiplex Infomax [paper] [code]
75. [WWW 2021] Graph Contrastive Learning with Adaptive Augmentation [paper] [code]
76. [WWW 2021] SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [paper] [code]
77. [Arxiv 2021] Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [paper] [code]
78. [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision [paper] [code]
79. [WSDM 2021] Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation [paper] [code]

1. [Arxiv 2020] COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking [paper] [code]
2. [Arxiv 2020] Distance-wise Graph Contrastive Learning [paper]
3. [Openreview 2020] Motif-Driven Contrastive Learning of Graph Representations [paper]
4. [Openreview 2020] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [paper]
5. [Openreview 2020] TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations [paper]
6. [Openreview 2020] Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [paper]
7. [Openreview 2020] Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization [paper]
8. [NeurIPS 2020] Self-Supervised Graph Transformer on Large-Scale Molecular Data [paper]
9. [NeurIPS 2020] Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs [paper] [code]
10. [NeurIPS 2020] Graph Contrastive Learning with Augmentations [paper] [code]
11. [Arxiv 2020] Self-supervised Learning on Graphs: Deep Insights and New Direction. [paper] [code]
12. [Arxiv 2020] Deep Graph Contrastive Representation Learning [paper]
13. [ICML 2020] When Does Self-Supervision Help Graph Convolutional Networks? [paper] [code]
14. [ICML 2020] Graph-based, Self-Supervised Program Repair from Diagnostic Feedback. [paper]
15. [ICML 2020] Contrastive Multi-View Representation Learning on Graphs. [paper] [code]
16. [ICML 2020 Workshop] Self-supervised edge features for improved Graph Neural Network training. [paper]
17. [Arxiv 2020] Self-supervised Training of Graph Convolutional Networks. [paper]
18. [Arxiv 2020] Self-Supervised Graph Representation Learning via Global Context Prediction. [paper]
19. [KDD 2020] GPT-GNN: Generative Pre-Training of Graph Neural Networks. [pdf] [code]
20. [KDD 2020] GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. [pdf] [code]
21. [Arxiv 2020] Graph-Bert: Only Attention is Needed for Learning Graph Representations. [paper] [code]
22. [ICLR 2020] InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. [paper] [code]
23. [ICLR 2020] Strategies for Pre-training Graph Neural Networks. [paper] [code]
24. [AAAI 2020] Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels. [paper]

1. [KDD 2019 Workshop] SGR: Self-Supervised Spectral Graph Representation Learning. [paper]
2. [ICLR 2019 Workshop] Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference. [paper]
3. [ICLR 2019 workshop] Pre-Training Graph Neural Networks for Generic Structural Feature Extraction. [paper]
4. [Arxiv 2019] Heterogeneous Deep Graph Infomax [paper] [code]
5. [ICLR 2019] Deep Graph Informax. [paper] [code]

(implicitly using self-supersvied learning or applying graph neural networks in other domains)

1. [Arxiv 2020] Self-supervised Learning: Generative or Contrastive. [paper]
2. [KDD 2020] Octet: Online Catalog Taxonomy Enrichment with Self-Supervision. [paper]
3. [WWW 2020] Structural Deep Clustering Network. [paper] [code]
4. [IJCAI 2019] Pre-training of Graph Augmented Transformers for Medication Recommendation. [paper] [code]
5. [AAAI 2020] Unsupervised Attributed Multiplex Network Embedding [paper] [code]
6. [WWW 2020] Graph representation learning via graphical mutual information maximization [paper]
7. [NeurIPS 2017] Inductive Representation Learning on Large Graphs [paper] [code]
8. [NeurIPS 2016 Workshop] Variational Graph Auto-Encoders [paper] [code]
9. [WWW 2015] LINE: Large-scale Information Network Embedding [paper] [code]
10. [KDD 2014] DeepWalk: Online Learning of Social Representations [paper] [code]