arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.
By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan
The paper introduces MOVE, a framework for multimodal open‑world verification and expansion in graph learning. MOVE jointly uses visual tokens, textual attributes, and graph context to identify nodes that cannot be assigned to existing classes, then employs a multimodal LLM to generate candidate class descriptions. It selectively expands the class space only when multimodal evidence consistently supports the new classes, avoiding redundancy, and reports an average 11.87% improvement across unknown recognition, open‑domain annotation, and downstream graph learning tasks.
By Zekai Chen, Jiayang Xing, Xun Wu, Miao Zhang, Xunkai Li, Kairui Yang, Zhengyu Wu, Xu Wang, Rong-Hua Li, Guoren Wang
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
The paper examines a multimodal approach that combines a self‑supervised GNN encoder with an alternating optimization scheme involving a language‑model teacher. Despite the expectation that this joint strategy would enhance predictive performance, the authors find that the combined model fails to deliver significant gains. They identify six key factors—ranging from anchor strength trade‑offs to misaligned representation spaces—that explain why the integration of text knowledge does not fully benefit graph learning.
By Fumiaki Kimino (SOKENDAI), Ryoma Sato (SOKENDAI, National Institute of Informatics)
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
arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.
By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan