8 papers
OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
Yuze Dai, Zhihan Zhang, Yan Zhao +6
Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality iss…
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)
Guanyi Qin, Jie Liang, Bingbing Zhang +50
In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Asses…
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…