Multi-view graph contrastive clustering via consensus constraint

Abstract

Multi-view graph clustering (MGC) aims to integrate graph information from multiple perspectives to determine a unified node partitioning scheme, thereby accurately mining the potential association rules of data. In recent years, contrastive learning-based MGC methods have attracted extensive attention due to their excellent generalization ability. However, existing mainstream edge-based strategies (neighborhood contrastive learning) still have significant limitations: (1) The search range for positives is fixed to the first-order neighborhood, which is overly restrictive and makes it difficult to fully explore the latent similarity between nodes; (2) Excessive focus is placed on intra-view connection relationships while ignoring the mining of inter-view structural consistency, which tends to lead to the omission of positives with stable similarity to the anchor or the misclassification of low-similarity nodes as positives. To address the above limitations, this paper proposes a Multi-view Graph Contrastive Clustering via Consensus Constraint (MGC⁴). Specifically, the algorithm first leverages inter-view structural consistency to identify the consensus neighbors of anchors and their cross-view counterparts as high-quality positives. Second, it assigns adaptive contrastive weights according to the cross-view average graph distance between the positives and the anchor, distinguishing the importance of different positives. Extensive experiments on multiple real-world datasets demonstrate that MGC⁴ consistently outperforms current state-of-the-art baselines in both clustering accuracy and robustness.