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
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.
By Sirui Zhang, Xu Wang, Zhengyu Wu, Xunkai Li, Hongchao Qin
arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.
By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan
arXiv:2607. 23245v1 Announce Type: cross Abstract: Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients.
By Haochen Liang, Jie Zhang, Hideya Ochiai
arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.
By Zhengyu Wu, Hongchao Qin, Xunkai Li, Zekai Chen, Rong-Hua Li, Guoren Wang
The paper introduces FedCORE, a federated adaptation framework for multimodal graph foundation models that jointly optimizes perception (Encoder) and reasoning (GNN) modules via a shared low‑dimensional latent state. Unlike prior methods that freeze the Encoder, FedCORE allows both components to adapt together, addressing the dependency between multimodal evidence extraction and graph‑based relational reasoning. Experiments show that FedCORE significantly narrows the Encoder–GNN pairing gap, achieving an 80.7% reduction compared to independent joint adaptation.
By Zekai Chen, Xun Wu, Hailin Zhang, Xunkai Li, Yu Liu, Kairui Yang, Muyan Huang, Xuaner Chen, Rong-Hua Li, Guoren Wang