Decoupled Graph Experts for Robust Multimodal Learning with Missing Modalities

Multimodal clinical data routinely arrive with *missing* modalities. Bipartite graph formulations address this missingness by encoding patients and modality-derived units as a bipartite graph, but modality-level graph construction forces heterogeneous sub-signals within one modality to share an operator (*false grouping*) and separates related sub-signals across modalities (*false separation*). Both restrictions are costly for patients with missing modalities, who rely on evidence propagated from other patients. False separation places related evidence from a missing modality in a view the patient cannot reach, and false grouping mixes the evidence the patient does receive with unrelated sub-signals. Which signals share an operator should therefore be learned from data: signals that co-vary across modalities through a shared process should share a view, while co-variation within one modality, which acquisition effects confound, should not decide the grouping. We propose DECENT, which encodes each observed modality into index-aligned proto-tokens and learns the signal-node partition by self-supervised sparse routing, trained to route signals that co-vary across modalities to the same view. With this partition fixed, DECENT runs message passing on the resulting view-specific sample–signal graphs and fuses the views observed for each patient. On ADNI and MIMIC-IV, DECENT achieves the highest average macro-F1 and macro-AUC among the evaluated methods across four missingness settings. Partition controls with the tokenizer and graph encoder fixed attribute these gains to the learned partition. Post hoc analyses on ADNI further show that the learned views align more closely with disease-informed axes than with source modalities. Code is available at [https://anonymous.4open.science/r/DECENT-7202/](https://anonymous.4open.science/r/DECENT-7202/README.md).