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Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

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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. [WWW 2021] SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [paper] [code]
  2. [Arxiv 2021] Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [paper] [code]
  3. [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision [paper]
  4. [WSDM 2021] Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation [paper] [code]
  5. [Arxiv 2020] COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking [paper] [code]
  6. [Arxiv 2020] Distance-wise Graph Contrastive Learning [paper]
  7. [Arxiv 2020] Graph Contrastive Learning with Adaptive Augmentation [paper]
  8. [Openreview 2020] Motif-Driven Contrastive Learning of Graph Representations [paper]
  9. [Openreview 2020] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [paper]
  10. [Openreview 2020] TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations [paper]
  11. [Openreview 2020] Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [paper]
  12. [Openreview 2020] Self-supervised Graph-level Representation Learning with Local and Global Structure [paper]
  13. [NeurIPS 2020] Self-Supervised Graph Transformer on Large-Scale Molecular Data [paper]
  14. [NeurIPS 2020] Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs [paper] [code]
  15. [NeurIPS 2020] Graph Contrastive Learning with Augmentations [paper] [code]
  16. [Arxiv 2020] Self-supervised Learning on Graphs: Deep Insights and New Direction. [paper] [code]
  17. [Arxiv 2020] Deep Graph Contrastive Representation Learning [paper]
  18. [ICML 2020] When Does Self-Supervision Help Graph Convolutional Networks? [paper] [code]
  19. [ICML 2020] Graph-based, Self-Supervised Program Repair from Diagnostic Feedback. [paper]
  20. [ICML 2020] Contrastive Multi-View Representation Learning on Graphs. [paper] [code]
  21. [ICML 2020 Workshop] Self-supervised edge features for improved Graph Neural Network training. [paper]
  22. [Arxiv 2020] Self-supervised Training of Graph Convolutional Networks. [paper]
  23. [Arxiv 2020] Self-Supervised Graph Representation Learning via Global Context Prediction. [paper]
  24. [KDD 2020] GPT-GNN: Generative Pre-Training of Graph Neural Networks. [pdf] [code]
  25. [KDD 2020] GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. [pdf] [code]
  26. [Arxiv 2020] Graph-Bert: Only Attention is Needed for Learning Graph Representations. [paper] [code]
  27. [ICLR 2020] Strategies for Pre-training Graph Neural Networks. [paper] [code]
  28. [AAAI 2020] Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels. [paper]
  29. [KDD 2019 Workshop] SGR: Self-Supervised Spectral Graph Representation Learning. [paper]
  30. [ICLR 2019 Workshop] Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference. [paper]
  31. [ICLR 2019 workshop] Pre-Training Graph Neural Networks for Generic Structural Feature Extraction. [paper]

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] 1. [KDD 2020] Octet: Online Catalog Taxonomy Enrichment with Self-Supervision. [paper] 1. [WWW 2020] Structural Deep Clustering Network. [paper] [code] 1. [ICLR 2020] InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. [paper] [code] 1. [ICLR 2019] Deep Graph Informax. [paper] [code] 1. [IJCAI 2019] Pre-training of Graph Augmented Transformers for Medication Recommendation. [paper] [code] 1. [Arxiv 2019] Heterogeneous Deep Graph Infomax [paper] [code] 1. [AAAI 2020] Unsupervised Attributed Multiplex Network Embedding [paper] [code] 1. [WWW 2020] Graph representation learning via graphical mutual information maximization [paper] 1. [NeurIPS 2017] Inductive Representation Learning on Large Graphs [paper] [code] 1. [NeurIPS 2016 Workshop] Variational Graph Auto-Encoders [paper] [code] 1. [WWW 2015] LINE: Large-scale Information Network Embedding [paper] [code] 1. [KDD 2014] DeepWalk: Online Learning of Social Representations [paper] [code]

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