222 citations · 614 across the 66 of their papers we have counts for
45 papers · 1 filter
MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS
Aarya Vasantlal, Joshua Zolla, Chuxu Zhang
Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without cons…
SupraBench: A Benchmark for Supramolecular Chemistry
Tianyi Ma, Yijun Ma, Zehong Wang +6
Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications. However, designing host-guest systems remains time-cons…
ProPlay: Procedural World Models for Self-Evolving LLM Agents
Yijun Ma, Zehong Wang, Yiyang Li +5
Self-evolving agents are expected to improve through interaction without external supervision, but this remains difficult in partially observable environments where agents must exp…
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Safal Thapaliya, Jiatan Huang, Chuxu Zhang
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts…
Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents
Ziming Li, Jiatan Huang, Xiaoguang Guo +2
Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidence-, uncertainty-, or difficult…
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…
BHyGNN+: Unsupervised Representation Learning for Heterophilic Hypergraphs
Tianyi Ma, Yiyue Qian, Zehong Wang +3
Hypergraph Neural Networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterop…
OPBench: A Graph Benchmark to Combat the Opioid Crisis
Tianyi Ma, Yiyang Li, Yiyue Qian +4
The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this letha…
Generalizing GNNs with Tokenized Mixture of Experts
Xiaoguang Guo, Zehong Wang, Jiazheng Li +5
Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations. We show that stati…
Molecular Representations in Implicit Functional Space via Hyper-Networks
Zehong Wang, Xiaolong Han, Qi Yang +10
Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipel…
Graph is a Substrate Across Data Modalities
Ziming Li, Xiaoming Wu, Zehong Wang +6
Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is typically learned in a modality- and t…
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Xingbo Fu, Zhenyu Lei, Zihan Chen +3
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powe…
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…
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…
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…
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
Song Wang, Zhen Tan, Yaochen Zhu +2
Out-of-distribution (OOD) generalization on graphs aims at dealing with scenarios where the test graph distribution differs from the training graph distributions. Compared to i.i.d…
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…
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…
Instance-Aware Graph Prompt Learning
Jiazheng Li, Jundong Li, Chuxu Zhang
Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availab…
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…