RoleMAG: Learning Neighbor Roles in Multimodal Graphs
For researchers working on multimodal graph learning, RoleMAG addresses the problem of modality interference in shared message passing by explicitly modeling neighbor roles, offering a novel approach to improve performance on multimodal graph tasks.
RoleMAG introduces a role-aware propagation framework for multimodal attributed graphs that distinguishes neighbors as shared, complementary, or heterophilous, routing them through separate channels. It achieves state-of-the-art results on RedditS and Bili_Dance benchmarks and remains competitive on Toys.
Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared message passing on a single graph and implicitly assume that the same neighbors are equally useful for all modalities. In practice, neighbors that benefit one modality may interfere with another, blurring modality-specific signals under shared propagation. To address this issue, we propose RoleMAG, a multimodal graph framework that learns how different neighbors should participate in propagation. Concretely, RoleMAG distinguishes whether a neighbor should provide shared, complementary, or heterophilous signals, and routes them through separate propagation channels. This enables cross-modal completion from complementary neighbors while keeping heterophilous ones out of shared smoothing. Extensive experiments on three graph-centric MAG benchmarks show that RoleMAG achieves the best results on RedditS and Bili\_Dance, while remaining competitive on Toys. Ablation, robustness, and efficiency analyses further support the effectiveness of the proposed role-aware propagation design. Our code is available at https://anonymous.4open.science/r/RoleMAG-7EE0/