GRADE: Evidence-Aligned Adaptive Granularity for Structure-Aware Residual Diffusion in Incomplete Multi-View Clustering

Incomplete multi-view clustering aims to discover a consistent clustering structure from partially observed multi-view data. However, recovering missing representations still faces two key challenges: (i) relying solely on shared semantics from the observed views can preserve cross-view consistency but cannot fully characterize the local structure specific to the target view; and (ii) view heterogeneity makes it difficult to establish reliable semantic correspondences between local structures across views, hindering the acquisition of query-relevant local priors from the target view. To address these challenges, we propose GRADE. First, we construct local regions for each view at adaptive granularities and use jointly observed members as evidence to establish soft correspondences across views. This extends sparse pairwise evidence to adaptive local regions and enables the retrieval of query-relevant local priors from the target view. Second, we map the shared semantics of the observed views into a target-view base representation. Rather than directly generating the entire missing representation, GRADE uses the retrieved local priors to guide residual diffusion, generating only the target-view-specific local information not captured by the base representation. Experiments on eight multi-view benchmark datasets across multiple missing rates demonstrate the superior overall clustering performance of GRADE.