21 citations · 63 across the 35 of their papers we have counts for
16 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…
NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering
Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan +4
Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chr…
MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference
Zheyuan Zhang, Lin Ge, Hongjiang Li +3
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, and LLM-based agents further extend these abilities to various practical workflows. Whi…
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…
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…
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang +2
What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that existing preference-alignment approache…
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…
EfficientLLM: Efficiency in Large Language Models
Zhengqing Yuan, Weixiang Sun, Yixin Liu +13
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…
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…
PsyScam: A Benchmark for Psychological Techniques in Real-World Scams
Shang Ma, Tianyi Ma, Jiahao Liu +4
Over the years, online scams have grown dramatically, with nearly 50% of global consumers encountering scam attempts each week. These scams cause not only significant financial los…
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
Jiazheng Li, Lu Yu, Qing Cui +4
High-quality data plays a critical role in the pretraining and fine-tuning of large language models (LLMs), even determining their performance ceiling to some degree. Consequently,…
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…
Adaptive Expansion for Hypergraph Learning
Tianyi Ma, Yiyue Qian, Shinan Zhang +2
Hypergraph, with its powerful ability to capture higher-order relationships, has gained significant attention recently. Consequently, many hypergraph representation learning method…
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…