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arXiv Machine Learning By Xunkai Li, Chenxi Wan, Yinlin Zhu, Wang Luo, Hongchao Qin, Rong-Hua Li, Guoren Wang

Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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