Abstract

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.

Bio: 

Annie Qu

Chancellor’s Professor, Department of Statistics, University of California Irvine

Ph.D., Statistics, the Pennsylvania State University

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 on personalized medicine, text mining, recommender systems, medical imaging data and network data analyses for complex heterogeneous data. The newly developed methods are able to extract essential and relevant information from large volume high-dimensional data. Her research has impacts in many fields such as biomedical studies, genomic research, public health research, social and political sciences.

Before she joins the UC Irvine, Dr. Qu is 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 as Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC, a recipient of the NSF Career award in 2004-2009. She is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and a Fellow of American Association for the Advancement of Science. She is also a recipient of Medallion Award and Lecturer. She is JASA Theory and Methods co-editor in 2023-2025.