Multi-view Graph Clustering with Partial Information Decomposition

Multi-view graph clustering aims to partition graph elements, including graphs and nodes, by exploring diverse views with distinct semantic topology patterns. Existing methods obtain discriminative representations mainly by integrating multi-view features or modeling consistency across views. However, these strategies assume cross-view information is entirely shared, which ignores other possible types of interaction, degrading the robustness and applicability of clustering. To this end, we propose a multi-view graph clustering framework based on partial information decomposition (PIDGC) that enables communication between multiple graph views in a universal space. Instead of enforcing restrictions within or across views, we achieve alignment of multiple graph views by optimizing the mutual information between original views and corresponding augmented counterparts, exploring redundant interactions between different views. We provide a theoretical illustration to demonstrate that mutual information can naturally explore diverse cross-view information via partial information decomposition, improving the discrimination of representations and clustering performance. Extensive experiments on multifarious datasets demonstrate the effectiveness and advancement of PIDGC compared with state-of-the-art baselines.