21 citations · 63 across the 35 of their papers we have counts for
39 papers
On the Safety of Graph Representation Learning
Xiaoguang Guo, Zehong Wang, Ziming Li +5
Graph representation learning (GRL) has evolved from topology-only graph embeddings to task-specific supervised GNNs, and more recently to reusable representations and graph founda…
GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health
Jiatan Huang, Zheyuan Zhang, Tianyi Ma +4
Nutritional interventions are important for managing chronic health conditions, but current computational methods provide limited support for personalized dietary guidance. We iden…
LongDA: Benchmarking LLM Agents for Long-Document Data Analysis
Yiyang Li, Zheyuan Zhang, Tianyi Ma +4
We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume we…
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…
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le +9
Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged…
Training MLPs on Graphs without Supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges…
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and s…
Can LLMs Convert Graphs to Text-Attributed Graphs?
Zehong Wang, Sidney Liu, Zheyuan Zhang +3
Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data…
GFT: Graph Foundation Model with Transferable Tree Vocabulary
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla +2
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Gr…