Multi-View Graph Clustering via Dual View-Cluster-Order Interactivity Mining

Junjie Liang, Xia Dong, Penglei Wang, Jin Xu, Danyang Wu, Feiping Nie

Abstract: Multi-view Graph Clustering (MGC) is a crucial approach for uncovering complex data structures by leveraging multiple perspectives of data. However, existing MGC methods face two key challenges: (1) limitations in graph structure that neglect long-range dependencies, and (2) overlooking the view-cluster local structure when mining view discrepancies. To address these issues, we propose a Multi-view Graph Clustering approach based on Dual View-Cluster-Order Interactivity (DVCOI-MGC). This approach consists of three modules: (1) Multi-View Multi-Order Graph Construction, where high-order graphs are generated using matrix exponentiation to capture long-range dependencies; (2) Dual View-Cluster-Order Interactivity, which utilizes a discrete graph cut model to separately learn order-specific and view-specific clustering results from the sets of order-specific multi-view graphs and view-specific multi-order graphs, with a separate View-Cluster-Order tensor weight for each learning direction; and (3) Bidirectional Truncation Consistency Learning, which applies a sparse boolean weight vector to locally select and integrate clustering results while preserving both the view-cluster and order-cluster local structures. Additionally, we introduce an efficient iterative optimization method to solve the discrete graph cut problem and provide a theoretical analysis of its convergence and computational complexity. Extensive experiments on 8 real-world datasets demonstrate that our approach significantly improves clustering performance over 11 state-of-the-art methods.

External IDs:doi:10.1109/tcsvt.2025.3588394