Unsupervised Cross-view Message Passing Method for Multi-view Graph Clustering

In recent years, multi-view graph clustering (MVGC) has attracted increasing attention from researchers. However, many existing MVGC methods focus on view-level integration through strategies like assigning weights to different views, for example, ignoring cross-view interactions between nodes. In fact, cross-view interactions at node level are crucial for extraction and fusion of semantic information. Additionally, some methods separate representation learning from clustering, which results in suboptimal clustering performance. To address these problems, we propose a novel unsupervised cross-view message passing method for MVGC. The kernel of our method is the cross-view interaction mechanism, which dynamically constructs node-specific cross-view edges based on node features and structural information. The mechanism enables adaptive interactions of informative nodes from different views, which promotes the extraction and propagation of complementary information. Besides, our method unifies representation learning and hyperspherical clustering in an end-to-end framework, which projects node representations into a hypersphere space, thereby enabling direct acquisition of balanced clustering results without dependence on external clustering methods. We provide comprehensive analyses on our method, and evaluate our method on six multi-view datasets. The results show that our method consistently achieves superior performance than existing state-of-the-art multi-view clustering methods.