mlsanityData to 14 Oct 2026

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

arXiv 2309.00024 ↗First posted 31 Aug 2023cs.LGcited by 5

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Abstract (arXiv)

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 30 of its 83 references that are in the map. First posted on arXiv 31 Aug 2023.

Builds on

30 of 83 references.

References · 30

  1. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  2. 2020Lite Transformer with Long-Short Range Attentioncited by 127
  3. 2016A Survey of Multi-View Representation Learningcited by 107
  4. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  5. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  6. 2022Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understandingcited by 91
  7. 2021Multi-level Feature Learning for Contrastive Multi-view Clusteringcited by 73
  8. 2019Large-scale Multi-view Subspace Clustering in Linear Timecited by 66
  9. 2022Hard Sample Aware Network for Contrastive Deep Graph Clusteringcited by 61
  10. 2023Cluster-guided Contrastive Graph Clustering Networkcited by 60
  11. 2022Simple Contrastive Graph Clusteringcited by 56
  12. 2020Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusioncited by 53
18 more
  1. 2019Ultra-Scalable Spectral Clustering and Ensemble Clusteringcited by 50
  2. 2023Dink-Net: Neural Clustering on Large Graphscited by 38
  3. 2018Robust Graph Learning from Noisy Datacited by 34
  4. 2023Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignmentcited by 33
  5. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
  6. 2022Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicitycited by 28
  7. 2023Deep Temporal Graph Clusteringcited by 28
  8. 2023Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignmentcited by 25
  9. 2022Mixed Graph Contrastive Network for Semi-Supervised Node Classificationcited by 21
  10. 2023Auto-weighted Multi-view Clustering for Large-scale Datacited by 20
  11. 2023Fast Continual Multi-View Clustering with Incomplete Viewscited by 19
  12. 2022Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graphcited by 16
  13. 2022Multiple Kernel Clustering with Dual Noise Minimizationcited by 10
  14. 2023One-step Multi-view Clustering with Diverse Representationcited by 10
  15. 2020Market2Dish: Health-aware Food Recommendationcited by 8
  16. 2023Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoningcited by 8
  17. 2019Multiple Partitions Aligned Clusteringcited by 5
  18. 2021Multiple Graph Learning for Scalable Multi-view Clusteringcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filteringcited by 14
  2. 2025Angular Constraint Embedding via SpherePair Loss for Constrained Clusteringcited by 2
  3. 2025LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clusteringcited by 0
  4. 2026Weighted Graph Clustering via Scale Contraction and Graph Structure Learningcited by 0
  5. 2026Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervisioncited by 0