TY - JOUR
T1 - Triangle Topology Enhancement for Multi-view Graph Clustering
AU - Wu, Danyang
AU - Wang, Penglei
AU - Lu, Jitao
AU - Hu, Zhanxuan
AU - Zhang, Hongming
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2025 IEEE. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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 (T2E) 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-T2E, 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-T2E achieves state-of-the-art performances compared with a mass of current competitors.
AB - 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 (T2E) 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-T2E, 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-T2E achieves state-of-the-art performances compared with a mass of current competitors.
KW - Clustering
KW - Multi-view Graph Clustering
KW - Multiview Learning
UR - http://www.scopus.com/inward/record.url?scp=105004326221&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2025.3566387
DO - 10.1109/TKDE.2025.3566387
M3 - 文章
AN - SCOPUS:105004326221
SN - 1041-4347
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
ER -