mlsanityData to 14 Oct 2026

CDC: A Simple Framework for Complex Data Clustering

arXiv 2403.03670 ↗First posted 6 Mar 2024cs.LGcited by 15

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Abstract (arXiv)

In today's data-driven digital era, the amount as well as complexity, such as multi-view, non-Euclidean, and multi-relational, of the collected data are growing exponentially or even faster. Clustering, which unsupervisely extracts valid knowledge from data, is extremely useful in practice. However, existing methods are independently developed to handle one particular challenge at the expense of the others. In this work, we propose a simple but effective framework for complex data clustering (CDC) that can efficiently process different types of data with linear complexity. We first utilize graph filtering to fuse geometry structure and attribute information. We then reduce the complexity with high-quality anchors that are adaptively learned via a novel similarity-preserving regularizer. We illustrate the cluster-ability of our proposed method theoretically and experimentally. In particular, we deploy CDC to graph data of size 111M.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 33 of its 65 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

33 of 65 references.

References · 33

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  5. 2016Variational Graph Auto-Encoderscited by 1,382
  6. 2018Deep Graph Infomaxcited by 1,106
  7. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  8. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  9. 2019Multi-scale Attributed Node Embeddingcited by 548
  10. 2020Deep Graph Contrastive Representation Learningcited by 539
  11. 2020Graph Clustering with Graph Neural Networkscited by 163
  12. 2019Attributed Graph Clustering: A Deep Attentional Embedding Approachcited by 149
21 more
  1. 2020Structural Deep Clustering Networkcited by 145
  2. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  3. 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
  4. 2019Learning Graph Embedding with Adversarial Training Methodscited by 84
  5. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  6. 2021Multi-view Contrastive Graph Clusteringcited by 76
  7. 2020Deep Fusion Clustering Networkcited by 71
  8. 2019Large-scale Multi-view Subspace Clustering in Linear Timecited by 66
  9. 2023Cluster-guided Contrastive Graph Clustering Networkcited by 60
  10. 2021Reconsidering Representation Alignment for Multi-view Clusteringcited by 57
  11. 2023Dink-Net: Neural Clustering on Large Graphscited by 38
  12. 2018Robust Graph Learning from Noisy Datacited by 34
  13. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
  14. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
  15. 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clusteringcited by 29
  16. 2022Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicitycited by 28
  17. 2021Consistent Multiple Graph Embedding for Multi-View Clusteringcited by 12
  18. 2023Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clusteringcited by 12
  19. 2020CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Datacited by 7
  20. 2021Improving Classification Accuracy with Graph Filteringcited by 3
  21. 2020Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 4.

15 papers.

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  1. 2025On the Benefits of Attribute-Driven Graph Domain Adaptationcited by 19
  2. 2025Homophily Enhanced Graph Domain Adaptationcited by 9
  3. 2024Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteinscited by 8
  4. 2025Unveiling Mode Connectivity in Graph Neural Networkscited by 3
  5. 2026Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structurecited by 3
  6. 2024textttSynC: Synergistic Boosting of Structure and Representation for Deep Graph Clusteringcited by 2
  7. 2024Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clusteringcited by 2
  8. 2025Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graphcited by 2
  9. 2024Causally-Aware Unsupervised Feature Selection Learningcited by 1
  10. 2025Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passingcited by 0
  11. 2025Attention Beyond Neighborhoods: Reviving Transformer for Graph Clusteringcited by 0
  12. 2025Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clusteringcited by 0
3 more
  1. 2026Weighted Graph Clustering via Scale Contraction and Graph Structure Learningcited by 0
  2. 2026Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergencecited by 0
  3. 2026Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learningcited by 0