arXiv Machine Learning June 15, 2026 By Sirui Zhang, Xu Wang, Zhengyu Wu, Xunkai Li, Hongchao Qin

Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs

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arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.

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arXiv Machine Learning

Jun 17

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.

By Zhengyu Wu, Xu Wang, Hongchao Qin, Xunkai Li, Guang Zeng, Rong-Hua Li, Guoren Wang

multimodal benchmarks safety

arXiv Machine Learning

Jul 20

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

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.

By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

fine-tuning multimodal safety

arXiv Machine Learning

Aug 10

TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution

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.

By Yinlin Zhu, Xunkai Li, Di Wu, Wang Luo, Miao Hu, Guocong Quan

rag multimodal benchmarks safety

arXiv Machine Learning

Sep 25

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective

GOMA (Graph-Optimized Multimodal Alignment) introduces a dual-embedding approach for multimodal retrieval, separating content embeddings supervised for paired identity from semantic embeddings trained with cross-modal pairs and observed relationships. The method fuses these embeddings, applies semantic agreement to weight graph edges, and uses restart graph propagation to reinforce the initial signal, enabling both single-modality and dual-attribute retrieval. Across six datasets and four tasks, GOMA outperforms 14 external methods on 14 primary metrics, with controlled experiments highlighting the impact of separate supervision, graph regularization, and semantic-guided propagation.

By Xu Wang, Xunkai Li, Yinlin Zhu, Rong-Hua Li, Guoren Wang

rag multimodal benchmarks safety

arXiv Machine Learning

Sep 10

Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach

arXiv:2609.06668v1 Announce Type: new Abstract: Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure an...

By Sirui Zhang, Yubing Zhou, Xunkai Li, Zekai Chen, Shumeng Li, Wang Luo, Yinlin Zhu, Yujin Gao, Rong-Hua Li

rag multimodal

arXiv Machine Learning

Aug 26

LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning

arXiv:2608.24795v1 Announce Type: new Abstract: Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multi...

By Xunkai Li, Zekai Chen, Zhengyu Wu, Henan Sun, Daohan Su, Guang Zeng, Hongchao Qin, Rong-Hua Li, Guoren Wang

multimodal benchmarks safety

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SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs

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

TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution

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LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning