SMGFM decomposes multimodal node signals on graphs into frequency bands via Chebyshev filters, routes them by topology reliability, and aligns objectives to preserve modality-specific semantics while achieving SOTA on MAG tasks.
Toward effective multimodal graph foundation model: A divide-and-conquer based approach,
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SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
SMGFM decomposes multimodal node signals on graphs into frequency bands via Chebyshev filters, routes them by topology reliability, and aligns objectives to preserve modality-specific semantics while achieving SOTA on MAG tasks.