Cross-View Granular-Ball Structure Transfer for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) groups unlabeled samples when one or more views are unavailable. Existing graph-based methods typically recover missing structure through instance-level pairwise neighborhoods, which are vulnerable to view-specific geometry and local noise. Granular balls instead aggregate locally coherent samples into mesoscopic units that preserve neighborhood organization while reducing sensitivity to isolated perturbations. Building on this insight, we propose Cross-View Granular-Ball Structure Transfer (CGBST), a region-mediated framework that transfers granular structure rather than missing features or individual relations. CGBST partitions observed embeddings into adaptive regions and constructs their local topology. Co-observed samples establish soft cross-view region correspondences, allowing source-view affiliations to infer target-region distributions for missing samples. The inferred affiliations and target topology are projected back to the sample level to recover missing relations. Experiments on four benchmarks and three missing rates show that CGBST achieves state-of-the-art performance in 42 of 48 evaluation settings, validating region-level structure transfer for incomplete multi-view clustering.