mlsanityData to 14 Oct 2026

CONVERT:Contrastive Graph Clustering with Reliable Augmentation

arXiv 2308.08963 ↗First posted 17 Aug 2023cs.LGcited by 23

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Abstract (arXiv)

Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distribution of a pre-defined augmentation to generate data-driven augmentations automatically. Although promising clustering performance has been achieved, we observe that these strategies still rely on pre-defined augmentations, the semantics of the augmented graph can easily drift. The reliability of the augmented view semantics for contrastive learning can not be guaranteed, thus limiting the model performance. To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT). Specifically, in our method, the data augmentations are processed by the proposed reversible perturb-recover network. It distills reliable semantic information by recovering the perturbed latent embeddings. Moreover, to further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network via quantifying the perturbation and recovery. Lastly, a label-matching mechanism is designed to guide the model by clustering information through aligning the semantic labels and the selected high-confidence clustering pseudo labels. Extensive experimental results on seven datasets demonstrate the effectiveness of the proposed method. We release the code and appendix of CONVERT at https://github.com/xihongyang1999/CONVERT on GitHub.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 17 Aug 2023.

Builds on

37 of 59 references.

References · 37

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  3. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  4. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  5. 2021Graph Contrastive Learning Automatedcited by 287
  6. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  7. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  8. 2019Attributed Graph Clustering: A Deep Attentional Embedding Approachcited by 149
  9. 2020Structural Deep Clustering Networkcited by 145
  10. 2022Towards Unsupervised Deep Graph Structure Learningcited by 114
  11. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  12. 2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
25 more
  1. 2020Adaptive Graph Encoder for Attributed Graph Embeddingcited by 89
  2. 2019Learning Graph Embedding with Adversarial Training Methodscited by 84
  3. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  4. 2020Deep Fusion Clustering Networkcited by 71
  5. 2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
  6. 2023Cluster-guided Contrastive Graph Clustering Networkcited by 60
  7. 2022Simple Contrastive Graph Clusteringcited by 56
  8. 2021Automated Self-Supervised Learning for Graphscited by 50
  9. 2023Dink-Net: Neural Clustering on Large Graphscited by 38
  10. 2023Neighbor Contrastive Learning on Learnable Graph Augmentationcited by 25
  11. 2023Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignmentcited by 25
  12. 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
  13. 2022Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learningcited by 23
  14. 2022Mixed Graph Contrastive Network for Semi-Supervised Node Classificationcited by 21
  15. 2020SAIL: Self-Augmented Graph Contrastive Learningcited by 20
  16. 2021Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influencescited by 19
  17. 2021Self-supervised Contrastive Attributed Graph Clusteringcited by 18
  18. 2022Test-Time Training for Graph Neural Networkscited by 17
  19. 2022Improved Dual Correlation Reduction Networkcited by 15
  20. 2023An Equivariant Generative Framework for Molecular Graph-Structure Co-Designcited by 10
  21. 2021Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filteringcited by 8
  22. 2023Message Intercommunication for Inductive Relation Reasoningcited by 8
  23. 2021Localized Graph Collaborative Filteringcited by 6
  24. 2022Cooperative Retriever and Ranker in Deep Recommenderscited by 6
  25. 2022Cache-Augmented Inbatch Importance Resampling for Training Recommender Retrievercited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 13; 2026: 4.

23 papers.

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  1. 2024Dual Test-time Training for Out-of-distribution Recommender Systemcited by 16
  2. 2024End-to-end Learnable Clustering for Intent Learning in Recommendationcited by 10
  3. 2025Homophily Enhanced Graph Domain Adaptationcited by 9
  4. 2025Understanding and Mitigating Under-Confidence in GNNs from the Final Layercited by 6
  5. 2025Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseasescited by 5
  6. 2025Unsupervised Graph Clustering with Deep Structural Entropycited by 4
  7. 2026KAT-Coder-V2.5 Technical Reportcited by 4
  8. 2023Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Informationcited by 3
  9. 2024Test-Time Training on Graphs with Large Language Models (LLMs)cited by 3
  10. 2025Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarioscited by 3
  11. 2024Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphscited by 2
  12. 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiationcited by 2
11 more
  1. 2025Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clusteringcited by 2
  2. 2024HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learningcited by 1
  3. 2025Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondencecited by 1
  4. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 1
  5. 2026PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documentscited by 1
  6. 2025Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphscited by 0
  7. 2025Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discoverycited by 0
  8. 2025GCL-GCN: Graphormer and Contrastive Learning Enhanced Attributed Graph Clustering Networkcited by 0
  9. 2025Attributed Graph Clustering with Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learningcited by 0
  10. 2025Contrastive clustering based on regular equivalence for influential node identification in complex networkscited by 0
  11. 2026Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learningcited by 0