Multiview Graph Clustering with Credible Signed Information

Current Multiview Graph Clustering (MGC) methods primarily focus on fusing multiple graphs and/or spectral embeddings to seek common clustering sets, however, the tendency between nodes and the locality between clusters and views are scarcely considered. Specifically, there are consistent topological structures in multiview graphs, which imply the tendency for pairwise nodes to be connected or disconnected across views. The locality refers to specific local structures, where some clusters exhibit clear discriminative patterns in partial views, whereas these patterns are broken in other views. With this awareness, we first propose a Credible Signed Information (CSI) extraction module to capture the tendency across all views and promote raw multiview graphs into signed multiview graphs. Building on CSI, we develop a novel mixed local graph cut model CSI-MGC to effectively capture the complex locality between clusters, views, and the signed graphs using a three-layer weight learning scheme. To solve the optimization problem involved in CSI-MGC, we propose an efficient discrete optimization algorithm and provide corresponding theoretical analyses. Finally, we conduct extensive experiments on eight benchmark datasets and against 13 state-of-the-art competitors and the results demonstrate the effectiveness of our proposals.