AI RESEARCH
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
arXiv CS.LG
•
ArXi:2603.27723v1 Announce Type: new Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks. However, our empirical analysis reveals inherent topology quality limitations in real-world MAGs, including noisy interactions, missing connections, and task-agnostic relational structures. A single graph derived from generic relationships is therefore unlikely to be universally optimal for diverse downstream tasks.