3 papers
cs.CL2026
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
Xingtong Yu, Shenghua Ye, Ruijuan Liang +4
Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…
cs.CL2025
GCoT: Chain-of-Thought Prompt Learning for Graphs
Xingtong Yu, Chang Zhou, Zhongwei Kuai +2
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raise…
cs.CL2025
SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation
Xingtong Yu, Zechuan Gong, Chang Zhou +2
Graphs are able to model interconnected entities in many online services, supporting a wide range of applications on the Web. This raises an important question: How can we train a…