7 papers
RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization
Siwei Zhang, Yun Xiong, Xi Chen +4
Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM agents to tackle complex tasks vi…
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Siwei Zhang, Yun Xiong, Yateng Tang +6
Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giv…
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
Yingying Wang, Yun Xiong, Xixi Wu +2
Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction hav…
Can Graph Learning Improve Planning in LLM-based Agents?
Xixi Wu, Yifei Shen, Caihua Shan +8
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests i…
ProCom: A Few-shot Targeted Community Detection Algorithm
Xixi Wu, Kaiyu Xiong, Yun Xiong +4
Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying f…
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Xi Chen, Yun Xiong, Siwei Zhang +7
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from bo…