Multi-View Graph Clustering via Node-Guided Contrastive Encoding

Multi-view clustering has gained significant attention for integrating multi-view information in multimedia applications. With the growing complexity of graph data, multi-view graph clustering (MVGC) has become increasingly important. Existing methods primarily use Graph Neural Networks (GNNs) to encode structural and feature information, but applying GNNs within contrastive learning poses specific challenges, such as integrating graph data with node features and handling both homophilic and heterophilic graphs. To address these challenges, this paper introduces Node-Guided Contrastive Encoding (NGCE), a novel MVGC approach that leverages node features to guide embedding generation. NGCE enhances compatibility with GNN filtering, effectively integrates homophilic and heterophilic information, and strengthens contrastive learning across views. Extensive experiments demonstrate its robust performance on six homophilic and heterophilic multi-view benchmark datasets.

Authors

• Yazhou Ren 0001
• Junlong Ke
• Zichen Wen
• Tianyi Wu
• Yang Yang 0002
• Xiaorong Pu
• Lifang He 0001

Keywords

• Multi-view Clustering
• Unsupervised Learning
• Graph Learning