计算机科学
聚类分析
理论计算机科学
图形
先验与后验
数据挖掘
算法
人工智能
认识论
哲学
作者
Jianpeng Chen,Yawen Ling,Jie Xu,Yazhou Ren,Shudong Huang,Xiaorong Pu,Zhifeng Hao,Philip S. Yu,Lifang He
标识
DOI:10.1109/tnnls.2024.3524205
摘要
Multiview graph clustering (MGC) methods are increasingly being studied due to the explosion of multiview data with graph structural information. The critical point of MGC is to better utilize view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose a variational graph generator for MGC (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then, a simple yet effective graph encoder in conjunction with the multiview clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of the VGMGC by analyzing the uncertainty of the inferred consensus graph with the information bottleneck (IB) principle. Extensive experiments demonstrate the superior performance of our VGMGC over state-of-the-art methods (SOTAs). The source code is publicly available at: https://github.com/cjpcool/VGMGC.
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