awesome-self-supervised-gnn

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).

Year 2021

1. [ICML 2021] Self-supervised Graph-level Representation Learning with Local and Global Structure [paper] [code]
2. [KDD 2021] MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge [paper]
3. [KDD 2021] Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning [paper] [code]
4. [arXiv 2021] Graph Barlow Twins: A self-supervised representation learning framework for graphs [paper]
5. [arXiv 2021] Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast [paper]
6. [arXiv 2021] Self-supervised on Graphs: Contrastive, Generative,or Predictive [paper]
7. [arXiv 2021] FedGL: Federated Graph Learning Framework with Global Self-Supervision [paper]
8. [arXiv 2021] Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning [paper]
9. [IJCNN 2021] Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation [paper]
10. [arXiv 2021] Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks [paper]
11. [arXiv 2021] Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities [paper]
12. [arXiv 2021] Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization [paper]
13. [arXiv 2021] Drug Target Prediction Using Graph Representation Learning via Substructures Contrast [paper]
14. [arXiv 2021] Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning [paper]
15. [arXiv 2021] Graph Self-Supervised Learning: A Survey [paper]
16. [arXiv 2021] Towards Robust Graph Contrastive Learning [paper]
17. [arXiv 2021] Pre-Training on Dynamic Graph Neural Networks [paper]
18. [arXiv 2021] Self-Supervised Learning of Graph Neural Networks: A Unified Review [paper]
19. [WWW 2021 Workshop] Iterative Graph Self-Distillation [paper]
20. [WWW 2021] Graph Contrastive Learning with Adaptive Augmentation [paper] [code]
21. [WWW 2021] SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [paper] [code]
22. [Arxiv 2021] Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [paper] [code]
23. [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision [paper] [code]
24. [WSDM 2021] Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation [paper] [code]

Year 2020

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]

Year 2019

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]

Other related papers

(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]