LEARNING TO DELETE: TASK-GUIDED MESSAGE-EFFECT INTERVENTION FOR HETEROPHILOUS MULTIMODAL GRAPH LEARNING

Multimodal graph models propagate multiple forms of node information over a shared topology, yet the utility of an incoming relation can vary with both the receiver and modality. Uniform aggregation cannot express this difference, whereas broad suppression may discard useful content or uniformly shrink neighborhood input. A controlled motivating study further shows that greater connectivity across classes coincides with lower classification performance, with markedly different sensitivity across graphs and host models. We propose Message Effect Intervention (MEI), which learns the task loss contrast induced by removing one exposed modality message from a frozen host. MEI aggregates modality specific estimates into an edge score, applies task specific candidate filtering available at deployment, changes at most one incoming edge bundle per receiver, and rescales retained coefficients to preserve their total mass. Training class prototypes provide the concrete filter for node classification settings. Its fixed message interface supports both the reference CM-MGNN host and compatible multimodal hosts without replacing their representations. The deployed operator admits structural guarantees for recovery, bundle coherence, receiver specific feasibility, coefficient mass preservation, and protected computation paths. On the original task settings, CM-MGNN improves over the strongest displayed baseline by 4.85% in Movies Macro F1, 3.46% in Bili Dance MRR, and 3.51% in CIDEr. MEI also improves every displayed compatible host pair under the fifteen percentage point condition.