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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).

Year 2021

  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]
  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]

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] 1. [KDD 2020] Octet: Online Catalog Taxonomy Enrichment with Self-Supervision. [paper] 1. [WWW 2020] Structural Deep Clustering Network. [paper] [code] 1. [IJCAI 2019] Pre-training of Graph Augmented Transformers for Medication Recommendation. [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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