5 citations · 12 across the 40 of their papers we have counts for
12 papers · 1 filter
Semantic Refinement with LLMs for Graph Representations
Safal Thapaliya, Zehong Wang, Jiazheng Li +3
Graph-structured data exhibit substantial heterogeneity in where their predictive signals originate: in some domains, node-level semantics dominate, while in others, structural pat…
Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use
Yiyang Li, Zehong Wang, Zhengqing Yuan +4
Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect…
AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering
Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan +6
Large language models (LLMs) and agent-based frameworks have advanced rapidly, enabling diverse applications. Yet, with the proliferation of models and agentic strategies, practiti…
Controllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidance
Jiachi Zhao, Zehong Wang, Yamei Liao +2
Graph generation is a fundamental problem in graph learning with broad applications across Web-scale systems, knowledge graphs, and scientific domains such as drug and material dis…
From Personal to Collective: On the Role of Local and Global Memory in LLM Personalization
Zehong Wang, Junlin Wu, ZHaoxuan Tan +4
Large language model (LLM) personalization aims to tailor model behavior to individual users based on their historical interactions. However, its effectiveness is often hindered by…
LLMs4All: A Review of Large Language Models Across Academic Disciplines
Yanfang Ye, Zheyuan Zhang, Tianyi Ma +26
Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown t…
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu, Zehong Wang, Zihan Chen +7
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent s…
AutoData: A Multi-Agent System for Open Web Data Collection
Tianyi Ma, Yiyue Qian, Zheyuan Zhang +9
The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, co…
Graph Foundation Models: A Comprehensive Survey
Zehong Wang, Zheyuan Liu, Tianyi Ma +16
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural lang…
Generative Graph Pattern Machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma +2
Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their…
LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection
Tianyi Ma, Yiyue Qian, Zehong Wang +3
As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants. These…
Beyond Message Passing: Neural Graph Pattern Machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downs…