Data to 7 Oct 2026

Deep Graph Contrastive Representation Learning

arXiv 2006.04131 ↗First posted 7 Jun 2020cs.LG stat.MLcited by 535

Not on the map. Why?

Abstract (arXiv)

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level. Specifically, we generate two graph views by corruption and learn node representations by maximizing the agreement of node representations in these two views. To provide diverse node contexts for the contrastive objective, we propose a hybrid scheme for generating graph views on both structure and attribute levels. Besides, we provide theoretical justification behind our motivation from two perspectives, mutual information and the classical triplet loss. We perform empirical experiments on both transductive and inductive learning tasks using a variety of real-world datasets. Experimental experiments demonstrate that despite its simplicity, our proposed method consistently outperforms existing state-of-the-art methods by large margins. Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.

Key facts. Cited by 535 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 57; 2022: 107; 2023: 121; 2024: 123; 2025: 77; 2026: 50.

535 papers.

Sort:

By area:

2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
2021Graph Self-Supervised Learning: A Surveycited by 289
2021Graph Contrastive Learning Automatedcited by 287
2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
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
2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
2022Data Augmentation for Deep Graph Learning: A Surveycited by 132

38 more

2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 131
2021An Empirical Study of Graph Contrastive Learningcited by 113
2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
2022What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoderscited by 82
2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 82
2021Mining Latent Structures for Multimedia Recommendationcited by 79
2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
2021Learning from Counterfactual Links for Link Predictioncited by 69
2022Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminationcited by 67
2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modelingcited by 63
2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 60
2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
2022Let Invariant Rationale Discovery Inspire Graph Contrastive Learningcited by 57
2022Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminatingcited by 57
2022Simple Contrastive Graph Clusteringcited by 56
2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49
2023Virtual Node Tuning for Few-shot Node Classificationcited by 46
2022Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrumcited by 43
2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
2022Adversarial Graph Contrastive Learning with Information Regularizationcited by 38
2022COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learningcited by 38
2022Spectral Augmentation for Self-Supervised Learning on Graphscited by 38
2023Dink-Net: Neural Clustering on Large Graphscited by 38
2021Prototypical Graph Contrastive Learningcited by 37
2023Simple and Asymmetric Graph Contrastive Learning without Augmentationscited by 37
2021Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Predictioncited by 36

50 most cited of 535 All 535 (JSON)