3 papers
cs.LG2026
Disentangled Graph Prompting for Out-Of-Distribution Detection
Cheng Yang, Yu Hao, Qi Zhang +1
When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-d…
cs.LG2026
Data-centric Prompt Tuning for Dynamic Graphs
Yufei Peng, Cheng Yang, Zhengjie Fan +1
Dynamic graphs have attracted increasing attention due to their ability to model complex and evolving relationships in real-world scenarios. Traditional approaches typically pre-tr…
cs.CL2025
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
Junze Chen, Cheng Yang, Shujie Li +4
Large language models (LLMs) have demonstrated their strong capabilities in various domains, and have been recently integrated for graph analysis as graph language models (GLMs). W…