Multi-view graph clustering via dual attention fusion and collaborative optimization.

Multi-view graph clustering, a fundamental task in data mining and machine learning, aims to partition nodes into disjoint groups by leveraging complementary information from multiple data sources. Although significant progress has been made, existing methods often struggle to effectively capture both the unique structural information within each view and the complementary relationships across different views. Moreover, the lack of mechanisms to enforce global semantic consistency frequently results in unstable consensus representations and degraded clustering quality. To address these issues, we propose a novel end-to-end method, Multi-view Graph Clustering via Dual attention fusion and Collaborative optimization (MGCDC). Specifically, each view is first encoded using a graph attention autoencoder to obtain view-specific node embeddings. These embeddings are then integrated via a view-level attention mechanism to generate a unified consensus representation. To guide the learning process, we introduce two collaborative optimization objectives. First, a cross-view cluster alignment loss is employed to jointly perform self-training learning on both the view-specific and consensus embeddings. Second, a semantic consistency enhancement loss is introduced to maximize mutual information between node embeddings and their corresponding cluster summaries. The entire model is optimized end-to-end by jointly learning node representations, integrating multi-view information, and refining cluster assignments. Extensive experiments on five benchmark datasets demonstrate that MGCDC achieves highly competitive performance compared to state-of-the-art methods.