Topology-Distilled Prompt Propagation for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to learn clusters from samples with partially observed views. Existing methods usually follow two directions, completing missing features or aligning the available views without imputation. Feature completion may inject biased synthetic signals because the missing view is not uniquely determined by the observed ones. Imputation-free methods avoid raw reconstruction, but they often rely on static consistency or pairwise alignment among observed views, which provides limited sample-specific guidance for refining missing-view representations. These limitations motivate a routing perspective in which the model learns how observed-view cues are encoded, exchanged, and fused across samples and missing patterns, rather than prescribing this process through a completion target or fixed alignment rule. We propose Topology-Distilled Prompt Propagation (TDPP), a framework that formulates missing-view representation learning as context-conditioned latent refinement. Its core module, cross-view prompt propagation, encodes observed-view cues into instance-specific prompts and iteratively exchanges them across views and samples under observation masks. This process enables missing-view representations to absorb reliable context while blocking messages from unobserved sources. To keep the propagated context consistent with the clustering objective, a self-supervised teacher trained on fully observed samples provides reference representations for the student. The teacher further guides the student with quality-aware feature distillation to account for unequal view reliability and sparse multi-hop topology distillation to preserve the neighborhood structure learned from complete data. Experiments on benchmark datasets show that TDPP outperforms recent state-of-the-art IMVC methods across missing ratios.