MGRL-IMVC: Multi-Granularity Relational Learning for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to uncover latent cluster structures from partially observed views. However, missing views weaken instance-level cross-view supervision, noisy observations reduce the reliability of available pairs, and cluster-level semantic discrepancies across views hinder consistent representation learning. This paper proposes MGRL-IMVC, a multi-granularity relational learning framework that avoids imputing missing features and jointly models sample relations, neighborhood structures, and prototype correspondences. Specifically, unified-space relational learning aggregates all observable representations and mines latent positive and negative relations, enabling samples without direct cross-view pairs to participate in contrastive learning. Cross-view structural learning constructs affinity-based neighborhood weights to provide structural evidence beyond direct similarity for same-instance pairs and to modulate contrastive constraints. Cross-view soft prototype alignment estimates soft correspondences between view-specific prototypes via the co-occurrence of soft assignments on commonly observed samples, and enforces bidirectional prototype matching for cluster-level semantic alignment. These objectives are jointly optimized with view reconstruction, allowing limited observations to complement each other across multiple relational granularities. Experiments on five benchmark datasets under various missing rates demonstrate that MGRL-IMVC achieves superior clustering accuracy and adaptability compared with seven representative deep IMVC methods, and ablation studies verify the complementary contributions of the three granularities.