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| Contrastive Collaborative Multi-view Attribute Graph Clustering Based on Adaptive Structure Enhancement |
| WANG Jinghong1,2,3, CHEN Xiao1,3,4,5, WANG Xizhao6, WANG Xu1,3,4,5, YANG Hongbo1,3,4,5, WANG Wei1,3 |
1. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024;
2. College of Artificial Intelligence, Hebei University of Engineering and Technology, Shijiazhuang 050020;
3. State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei 230088;
4. Hebei Provincial Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang 050024;
5. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Hebei Normal University, Shijiazhuang 050024;
6. College of Computer Science and Software Engineering, Shen-zhen University, Shenzhen 518060 |
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Abstract Most clustering methods mainly focus on single-view data, while the research on multi-view clustering remains relatively under-explored. Existing multi-view clustering methods often emphasize learning inter-view information while neglecting the thorough exploitation of intra-view information. In this paper, a contrastive collaborative multi-view attribute graph clustering based on adaptive structure enhancement(ACCMVC) is proposed. First, an adaptive structure enhancement strategy is designed to generate edge weights by combining node importance and the complex relationships among node features. These edge weights are applied to construct new adjacency matrices for the views, and thereby structure-enhanced graphs are generated. Second, edge weights are introduced into neighborhood contrastive learning. Intra-view enhanced neighborhood contrastive learning is applied to views and their structure-enhanced graphs, while inter-view enhanced neighborhood contrastive learning is utilized among multiple views. Finally, considering the varying view importance, an attention mechanism is introduced to calculate the weight of each view for effective fusion. Experiments on multiple datasets demonstrate that ACCMVC achieves superior clustering performance.
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Received: 08 July 2025
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| Fund:National Natural Science Foundation of China(No.NSFC62376161,U24A20322), Natural Science Foundation of Hebei Province(No.F20242050280), Graduate Innovation Fund of Hebei Province(No.CXZZSS2025049), Science and Technology Research Fund of Hebei Normal University(No.L2024C05,L2022C02), Open Fund of State Key Laboratory of Cognitive Intelligence of iFLYTEK(No.COGOS-2025HE07) |
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Corresponding Authors:
WANG Jinghong, Ph.D., professor. Her research interests include machine learning and data mining.
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About author:: CHEN Xiao, Master student. Her research interests include data mining and graph representation learning.
WANG Xizhao, Ph.D., professor. His research interests include machine learning and uncertainty modeling.
WANG XU, Master student. His research interests include graph representation lear-ning.
YANG Hongbo, Master student. His research interests include information security.
WANG Wei, Ph.D., associate professor. His research interests include artificial intelligence |
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2025, Vol. 38 
