Triangle Topology Enhancement for Multi-View Graph Clustering

Danyang Wu, Penglei Wang, Jitao Lu, Zhanxuan Hu, Hongming Zhang, Feiping Nie

Abstract: Most existing multi-view graph clustering models focus on integrating the topological structure of different views directly, which cannot efficiently stimulate the collaboration between multiple views. To alleviate this problem, this paper proposes a Triangle Topology Enhancement (T$^{2}$E) module, which expands two topological structures based on the raw topology of each view, including the self-triangle enhanced topology that highlights the local view information and the cross-view triangle enhanced topology containing the global-local view information. Afterward, this paper designs a novel multi-view graph clustering model, named MGC-T$^{2}$E, to integrate both the raw and derived topological structures and directly induce consistent clustering indicators based on a self-supervised clustering module. In the simulation, the experimental results demonstrate that MGC-T$^{2}$E achieves state-of-the-art performances compared with a mass of current competitors.

External IDs:dblp:journals/tkde/WuWLHZN25