arXiv Machine Learning
Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach
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TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
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SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
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Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
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Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach
The paper introduces GraphBind, a topology-driven method for multimodal graph foundation models that binds heterogeneous node modalities into a unified shared space using graph topology. By leveraging stable graph structure to organize self and neighborhood semantics, GraphBind adapts this integrated space for both discriminative and generative tasks. Experiments against 11 baselines show that GraphBind outperforms them, achieving up to 28.1% relative improvement on key tasks.