Data to 7 Oct 2026

Graph Contrastive Learning with Augmentations

arXiv 2010.13902 ↗First posted 22 Oct 2020cs.LG cs.AIcited by 1,203

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Abstract (arXiv)

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at https://github.com/Shen-Lab/GraphCL.

Key facts. Cited by 1,203 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 114; 2022: 221; 2023: 245; 2024: 285; 2025: 197; 2026: 141.

1,203 papers.

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2022Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learningcited by 553
2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
2021Graph Self-Supervised Learning: A Surveycited by 289
2021Graph Contrastive Learning Automatedcited by 287
2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166

38 more

2022Protein Representation Learning by Geometric Structure Pretrainingcited by 161
2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
2023All in One: Multi-task Prompting for Graph Neural Networkscited by 147
20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 145
2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 137
2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
2022Data Augmentation for Deep Graph Learning: A Surveycited by 132
2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 131
2022A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Languagecited by 129
2023MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adaptercited by 128
2021Self-supervised Graph-level Representation Learning with Local and Global Structurecited by 125
2022Pre-training via Denoising for Molecular Property Predictioncited by 124
2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
2021An Empirical Study of Graph Contrastive Learningcited by 113
2022Towards Unsupervised Deep Graph Structure Learningcited by 112
2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
2022Universal Prompt Tuning for Graph Neural Networkscited by 100
2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
2023PRODIGY: Enabling In-context Learning Over Graphscited by 97
2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
2023InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discoverycited by 86
2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
2024Position: Graph Foundation Models are Already Herecited by 84
2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 84
2024ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphscited by 83
2022What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoderscited by 82
2022One Transformer Can Understand Both 2D & 3D Molecular Datacited by 77
2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76

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