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arXiv · 2604.12271

RoleMAG: Learning Neighbor Roles in Multimodal Graphs

Yilong Zuo·Xunkai Li·Zhihan Zhang·Ronghua Li·Guoren Wang
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Abstract

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/

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Yilong Zuo, Xunkai Li, Zhihan Zhang, Ronghua Li, Guoren Wang. 2026-04-14. RoleMAG: Learning Neighbor Roles in Multimodal Graphs. https://arxiv.org/abs/2604.12271

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