6 citations · 19 across the 16 of their papers we have counts for
4 papers · 1 filter
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Haohua Niu, Xingtong Yu, Yang Liu +6
Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move tow…
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…
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…
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Xingtong Yu, Chang Zhou, Yuan Fang +1
Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph…