6 papers · 1 filter
RoleMAG: Learning Neighbor Roles in Multimodal Graphs
Yilong Zuo, Xunkai Li, Zhihan Zhang +2
Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared message passing on a single graph…
OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport
Yilong Zuo, Xunkai Li, Zhihan Zhang +3
Multimodal Attributed Graphs (MAGs) have been widely adopted for modeling complex systems by integrating multi-modal information, such as text and images, on nodes. However, we ide…
BoostFGL: Boosting Fairness in Federated Graph Learning
Zekai Chen, Kairui Yang, Xunkai Li +6
Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data. While existing FGL methods o…
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
Zhihan Zhang, Xunkai Li, Yilong Zuo +5
Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantic…
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
Enjun Du, Xunkai Li, Tian Jin +3
The era of foundation models has revolutionized AI research, yet Graph Foundation Models (GFMs) remain constrained by the scarcity of large-scale graph corpora. Traditional graph d…
LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models
Zhihan Zhang, Xunkai Li, Lei Zhu +6
The emergence of large language models (LLMs) has popularized text-attributed graphs (TAGs), creating an urgent need for graph structure learning (GSL) methods that effectively lev…