6 papers
Gaussian Relational Graph Transformer
Zezhong Ding, Jin Li, Xugang Wang +1
Relational graph learning models relational databases as graphs and has demonstrated superior performance on a wide range of relational predictive tasks. However, existing methods…
Learning Graph Foundation Models on Riemannian Graph-of-Graphs
Haokun Liu, Zezhong Ding, Xike Xie
Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across diverse graph tasks and dom…
See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Models
Shuo Han, Yukun Cao, Zezhong Ding +3
Vision-language models (VLMs) have shown promise in graph structure understanding, but remain limited by input-token constraints, facing scalability bottlenecks and lacking effecti…
DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion
Jin Li, Zezhong Ding, Xike Xie
Knowledge graphs (KGs) are vital for enabling knowledge reasoning across various domains. Recent KG reasoning methods that integrate both global and local information have achieved…
SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
Shangyou Wang, Zezhong Ding, Xike Xie
Graph Neural Networks (GNNs) have shown remarkable success in graph classification tasks by capturing both structural and feature-based representations. However, real-world graphs…
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
Yukun Cao, Shuo Han, Zengyi Gao +3
Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph de…