mlsanityData to 7 Oct 2026

Dink-Net: Neural Clustering on Large Graphs

arXiv 2305.18405 ↗First posted 28 May 2023cs.LG cs.AIcited by 38

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Abstract (arXiv)

Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the existing methods fail to scale to the large graph with million nodes. To solve this problem, a scalable deep graph clustering method (Dink-Net) is proposed with the idea of dilation and shrink. Firstly, by discriminating nodes, whether being corrupted by augmentations, representations are learned in a self-supervised manner. Meanwhile, the cluster centres are initialized as learnable neural parameters. Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner. By these settings, we unify the two-step clustering, i.e., representation learning and clustering optimization, into an end-to-end framework, guiding the network to learn clustering-friendly features. Besides, Dink-Net scales well to large graphs since the designed loss functions adopt the mini-batch data to optimize the clustering distribution even without performance drops. Both experimental results and theoretical analyses demonstrate the superiority of our method. Compared to the runner-up, Dink-Net achieves 9.62% NMI improvement on the ogbn-papers100M dataset with 111 million nodes and 1.6 billion edges. The source code is released at https://github.com/yueliu1999/Dink-Net. Besides, a collection (papers, codes, and datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 50 of its 83 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

50 of 83 references.

References · 50

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 13; 2025: 9; 2026: 5.

38 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023CONVERT:Contrastive Graph Clustering with Reliable Augmentationcited by 23
  3. 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clusteringcited by 20
  4. 2023DealMVC: Dual Contrastive Calibration for Multi-view Clusteringcited by 19
  5. 2023Reinforcement Graph Clustering with Unknown Cluster Numbercited by 16
  6. 2024Self-Supervision Improves Diffusion Models for Tabular Data Imputationcited by 16
  7. 2024CDC: A Simple Framework for Complex Data Clusteringcited by 15
  8. 2025Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filteringcited by 14
  9. 2023One-step Multi-view Clustering with Diverse Representationcited by 10
  10. 2024End-to-end Learnable Clustering for Intent Learning in Recommendationcited by 10
  11. 2023TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classificationcited by 9
  12. 2023Scalable Incomplete Multi-View Clustering with Structure Alignmentcited by 7
26 more
  1. 2024Effective Clustering on Large Attributed Bipartite Graphscited by 7
  2. 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspectivecited by 7
  3. 2023Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learningcited by 6
  4. 2024Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learningcited by 6
  5. 2023Efficient Multi-View Graph Clustering with Local and Global Structure Preservationcited by 5
  6. 2024THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplingscited by 5
  7. 2024GraphFM: A Comprehensive Benchmark for Graph Foundation Modelcited by 4
  8. 2024Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Predictioncited by 4
  9. 2025CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoderscited by 4
  10. 2025ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Spacecited by 4
  11. 2023arXiv4TGC: Large-Scale Datasets for Temporal Graph Clusteringcited by 3
  12. 2023Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?cited by 3
  13. 2024Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clusteringcited by 3
  14. 2026Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clusteringcited by 3
  15. 2026KAT-Coder-V2.5 Technical Reportcited by 3
  16. 2024textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clusteringcited by 2
  17. 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiationcited by 2
  18. 2025Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondencecited by 1
  19. 2026Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasetscited by 1
  20. 2024One-Step Late Fusion Multi-view Clustering with Compressed Subspacecited by 0
  21. 2025Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attentioncited by 0
  22. 2025Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphscited by 0
  23. 2025Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passingcited by 0
  24. 2025Contrastive clustering based on regular equivalence for influential node identification in complex networkscited by 0
  25. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 0
  26. 2026RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clusteringcited by 0