【报告摘要】Graph neural networks (GNNs) have achieved promising performance in a variety of graph focused tasks. Despite their success, the two major limitations of existing GNNs are the capability of learning various-order representations and providing interpretability of such deep learning-based black-box models. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework. The proposed framework is able to extract knowledge from high-order neighbors, sequentially integrates information of various orders, and offers explanations for the learned model by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and important node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher complexity and showcase its power to represent the layer-wise neighborhood mixing. We conduct comprehensive numerical studies using both simulated data and a real-world case study on investigating the neural mechanisms of the rat hippocampus, demonstrating that the performance of MaGNet is competitive with state-of-the-art methods.

【报告人简介】Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027.

Qu Lab website: https://faculty.sites.uci.edu/qulab/