mlsanityData to 14 Oct 2026

Multi-view Contrastive Graph Clustering

arXiv 2110.11842 ↗First posted 22 Oct 2021cs.LG cs.AI cs.CVcited by 76

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Abstract (arXiv)

With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of two key steps: we first filter out the undesirable high-frequency noise while preserving the graph geometric features via graph filtering and obtain a smooth representation of nodes; we then learn a consensus graph regularized by graph contrastive loss. Results on several benchmark datasets show the superiority of our method with respect to state-of-the-art approaches. In particular, our simple approach outperforms existing deep learning-based methods.

Key facts. Cited by 76 papers in the map (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

19 of 35 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Variational Graph Auto-Encoderscited by 1,382
  4. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  5. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
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  7. 2020Contrastive Clusteringcited by 265
  8. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 214
  9. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  10. 2019Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learningcited by 94
  11. 2018Robust Graph Learning from Noisy Datacited by 34
  12. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
7 more
  1. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  2. 2017Multi-view Low-rank Sparse Subspace Clusteringcited by 28
  3. 2020Deep Robust Clustering by Contrastive Learningcited by 25
  4. 2017Principled Multilayer Network Embeddingcited by 24
  5. 2021Self-supervised Consensus Representation Learning for Attributed Graphcited by 15
  6. 2021Towards Clustering-friendly Representations: Subspace Clustering via Graph Filteringcited by 14
  7. 2019Structure Learning with Similarity Preservingcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 14; 2023: 23; 2024: 19; 2025: 9; 2026: 10.

76 papers.

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  1. 2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
  2. 2023Cluster-guided Contrastive Graph Clustering Networkcited by 60
  3. 2023Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignmentcited by 33
  4. 2022A Survey on Incomplete Multi-view Clusteringcited by 31
  5. 2023GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clusteringcited by 30
  6. 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clusteringcited by 29
  7. 2023PC-Conv: Unifying Homophily and Heterophily with Two-fold Filteringcited by 28
  8. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  9. 2024Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noisecited by 25
  10. 2022GLCC: A General Framework for Graph-Level Clusteringcited by 21
  11. 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clusteringcited by 21
  12. 2022Structure-Preserving Graph Representation Learningcited by 17
38 more
  1. 2022Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clusteringcited by 16
  2. 2023Reinforcement Graph Clustering with Unknown Cluster Numbercited by 16
  3. 2022Improved Dual Correlation Reduction Networkcited by 15
  4. 2024CDC: A Simple Framework for Complex Data Clusteringcited by 15
  5. 2023Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classificationcited by 12
  6. 2023Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clusteringcited by 12
  7. 2024When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learningcited by 12
  8. 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learningcited by 12
  9. 2024Robust Graph Structure Learning under Heterophilycited by 11
  10. 2021Stationary Diffusion State Neural Estimation for Multiview Clusteringcited by 10
  11. 2023Latent Graph Inference with Limited Supervisioncited by 9
  12. 2022High-order Multi-view Clustering for Generic Datacited by 8
  13. 2022Variational Graph Generator for Multi-View Graph Clusteringcited by 7
  14. 2024Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learningcited by 7
  15. 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspectivecited by 7
  16. 2023Enhanced Latent Multi-view Subspace Clusteringcited by 6
  17. 2024Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clusteringcited by 6
  18. 2024A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentationcited by 6
  19. 2023Unsupervised Multiplex Graph Learning with Complementary and Consistent Informationcited by 5
  20. 2024Provable Filter for Real-world Graph Clusteringcited by 5
  21. 2022Scalable Multi-view Clustering with Graph Filteringcited by 4
  22. 2022Subspace-Contrastive Multi-View Clusteringcited by 4
  23. 2023Signed Directed Graph Contrastive Learning with Laplacian Augmentationcited by 4
  24. 2023Self-Contrastive Graph Diffusion Networkcited by 4
  25. 2023Contrastive Continual Multi-view Clustering with Filtered Structural Fusioncited by 4
  26. 2023A Contrastive Variational Graph Auto-Encoder for Node Clusteringcited by 4
  27. 2025ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Spacecited by 4
  28. 2022EGRC-Net: Embedding-induced Graph Refinement Clustering Networkcited by 3
  29. 2023Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Networkcited by 3
  30. 2023A Unified Framework for Fair Spectral Clustering With Effective Graph Learningcited by 3
  31. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  32. 2026Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structurecited by 3
  33. 2022Dual Information Enhanced Multi-view Attributed Graph Clusteringcited by 2
  34. 2023A Prototype-Oriented Clustering for Domain Shift with Source Privacycited by 2
  35. 2023Incomplete Multi-view Clustering via Diffusion Completioncited by 2
  36. 2024HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapterscited by 2
  37. 2025Effective Clustering for Large Multi-Relational Graphscited by 2
  38. 2026Learning Disentangled Representations for Generalized Multi-view Clusteringcited by 2

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