arXiv Machine Learning July 20, 2026 By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

Read the original on arXiv Machine Learning →

arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs

arXiv:2606. 12867v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities.

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arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.

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