Optimal Transport Guided Dual-Distribution Constraints for Multi-view Graph Clustering

Multi-view graph clustering aims to group graph nodes by jointly exploiting multi-view node attributes and topological connections. However, existing methods lack reliable global supervision for cross-view representation alignment. Under severe inter-view noise and heterogeneity, representations of each view converge to distinct local optima and yield weak cross-view consensus. Furthermore, most works only regularize node features while neglecting graph topological neighborhood distributions, decoupling graph smoothing from clustering objectives. To address these issues, we propose a novel Optimal Transport Guided Dual-Distribution Constraints for Multi-view Graph Clustering (OT-DDC). It employs an optimal transport-based pseudo-label generation module to construct global cross-view self supervision and alleviate weak cross-view consensus. Meanwhile, the dual-distribution constraint clustering module jointly optimizes cluster centroids in the feature space and topology-aware neighborhood distribution centers, reducing misalignment between semantic representations and graph topological structures. Extensive experiments demonstrate the effectiveness of our algorithm.