TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
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. To address this challenge, we propose Task-aware Modality and Topology co-Evolution (TMTE), a novel MGL framework that jointly and iteratively optimizes graph topology and multimodal representations toward the target ...
| Authors | Yinlin Zhu, Xunkai Li, Di Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 7 August 2026 |
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