mlsanityData to 7 Oct 2026

GraphCL: Contrastive Self-Supervised Learning of Graph Representations

arXiv 2007.08025 ↗First posted 15 Jul 2020cs.LG stat.MLcited by 26

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Abstract (arXiv)

We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations of two randomly perturbed versions of the intrinsic features and link structure of the same node's local subgraph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 6; 2024: 4; 2025: 1; 2026: 2.

26 papers.

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  1. 2021Graph Self-Supervised Learning: A Surveycited by 289
  2. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
  3. 2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49
  4. 2022COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learningcited by 38
  5. 2021Graph Communal Contrastive Learningcited by 34
  6. 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
  7. 2021TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learningcited by 31
  8. 2023Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphscited by 23
  9. 2023When to Pre-Train Graph Neural Networks? From Data Generation Perspective!cited by 17
  10. 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networkscited by 14
  11. 2023Multi-behavior Self-supervised Learning for Recommendationcited by 11
  12. 2022Motif-based Graph Representation Learning with Application to Chemical Moleculescited by 9
14 more
  1. 2023Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learningcited by 9
  2. 2022Generative Subgraph Contrast for Self-Supervised Graph Representation Learningcited by 8
  3. 2021Temporal Contrastive Graph Learning for Video Action Recognition and Retrievalcited by 6
  4. 2022Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samplescited by 6
  5. 2024Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learningcited by 5
  6. 2025How to Use Graph Data in the Wild to Help Graph Anomaly Detection?cited by 3
  7. 2024Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learningcited by 2
  8. 2024Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalizationcited by 2
  9. 2022Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Modelscited by 1
  10. 2022Unified GCNs: Towards Connecting GCNs with CNNscited by 0
  11. 2023Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendationcited by 0
  12. 2024Graph-level Protein Representation Learning by Structure Knowledge Refinementcited by 0
  13. 2026MoleCode unlocks structural intelligence in large language modelscited by 0
  14. 2026Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Moleculescited by 0