128 citations · 405 across the 50 of their papers we have counts for
51 papers
A Mechanistic Study of Automated Failure Attribution in LLM-based Agentic Systems
Wendy Zheng, Liang Wu, Yinhan He +3
Agentic systems have demonstrated strong capabilities on complex tasks by enabling multiple interacting large language model (LLM)-based agents to collaborate and execute subtasks…
Machine-State Embeddings as an Operational Coordinate System for Accelerator Operation
Chris Tennant, Jundong Li, Song Wang
We demonstrate that graph neural network (GNN) embeddings of injector configurations provide a practical operational coordinate system for the Continuous Electron Beam Accelerator…
Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping
Zhenyu Lei, Qiong Wu, Jianxiong Dong +4
Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and mono…
BrainTAP: Brain Disorder Prediction with Adaptive Distill and Selective Prior Integration
Zhenyu Lei, Aiying Zhang, Song Wang +2
Predicting clinical outcomes from brain networks in large-scale neuroimaging cohorts such as the Adolescent Brain Cognitive Development (ABCD) study requires effectively integratin…
Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks
Song Wang, Zhenyu Lei, Zhen Tan +4
Nearly one in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder, underscoring the urgency of…
Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
Zhen Tan, Song Wang, Yifan Li +4
Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how the inputs lead to the predicti…
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
Song Wang, Junhong Lin, Xiaojie Guo +3
While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This lim…
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan +8
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and hum…
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization
Song Wang, Xiaodong Yang, Rashidul Islam +4
To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step…
KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
Zaiyi Zheng, Yushun Dong, Song Wang +3
Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classific…
Harnessing Large Language Models for Disaster Management: A Survey
Zhenyu Lei, Yushun Dong, Weiyu Li +3
Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs ha…
ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data
Zhenyu Lei, Yushun Dong, Jundong Li +1
Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningf…
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Yushun Dong, Patrick Soga, Yinhan He +2
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggreg…
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong +3
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing m…
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga +3
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs,…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
Understanding and Modeling Job Marketplace with Pretrained Language Models
Yaochen Zhu, Liang Wu, Binchi Zhang +5
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job…
Towards Certified Unlearning for Deep Neural Networks
Binchi Zhang, Yushun Dong, Tianhao Wang +1
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees…
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Yushun Dong, Binchi Zhang, Zhenyu Lei +2
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of…
A Benchmark for Fairness-Aware Graph Learning
Yushun Dong, Song Wang, Zhenyu Lei +4
Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware…
CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models
Song Wang, Peng Wang, Tong Zhou +3
As Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of L…
"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models
Zhen Tan, Chengshuai Zhao, Raha Moraffah +5
Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases, improving their performance in applications like fact-chec…