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Contrastive Multi-View Representation Learning on Graphs

arXiv 2006.05582 ↗First posted 10 Jun 2020cs.LG stat.MLcited by 651

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Abstract (arXiv)

We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by contrasting encodings from first-order neighbors and a graph diffusion. We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol. For example, on Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art. When compared to supervised baselines, our approach outperforms them in 4 out of 8 benchmarks. Source code is released at: https://github.com/kavehhassani/mvgrl

Key facts. Cited by 651 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 97; 2022: 157; 2023: 142; 2024: 137; 2025: 83; 2026: 35.

651 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 818
  2. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  3. 2021Graph Self-Supervised Learning: A Surveycited by 289
  4. 2021Graph Contrastive Learning Automatedcited by 287
  5. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  6. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
  7. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  8. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  9. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  10. 2024LLaGA: Large Language and Graph Assistantcited by 177
  11. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  12. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
38 more
  1. 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 171
  2. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  3. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  4. 2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 165
  5. 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
  6. 2021SCARF: Self-Supervised Contrastive Learning using Random Feature Corruptioncited by 151
  7. 2021Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learningcited by 140
  8. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  9. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  10. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
  11. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 131
  12. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  13. 2021Self-supervised Graph-level Representation Learning with Local and Global Structurecited by 125
  14. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  15. 2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
  16. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  17. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  18. 2021Trusted Multi-View Classificationcited by 94
  19. 2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
  20. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  21. 2024Position: Graph Foundation Models are Already Herecited by 84
  22. 2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 84
  23. 2022What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoderscited by 82
  24. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  25. 2021Multi-view Contrastive Graph Clusteringcited by 76
  26. 2021Multi-level Feature Learning for Contrastive Multi-view Clusteringcited by 73
  27. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  28. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  29. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  30. 2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
  31. 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learningcited by 70
  32. 2021Learning from Counterfactual Links for Link Predictioncited by 69
  33. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  34. 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
  35. 2022Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminationcited by 67
  36. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  37. 2021TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learningcited by 65
  38. 2021MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graphcited by 65

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