报告名称：Network Games Induced Prior for Graph Topology Learning

报告时间：5月26日16:00-17:00

报告地点：中北校区数学馆201

报告摘要:

Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate priors such as sparsity or modularity which highlight on the structural property of graph topology. We depart from these approaches to develop priors that are directly inspired by complex network dynamics. Focusing on social networks with actions modeled by equilibriums of linear quadratic games, we postulate that the social network topologies are optimized with respect to a social welfare function. Utilizing this prior knowledge, we propose a network games induced regularizer to assist graph learning. We then formulate the graph topology learning problem as a bilevel program. We develop a two-timescale gradient algorithm to tackle the latter. We draw theoretical insights on the optimal graph structure of the bilevel program and show that they agree with the topology in several man-made networks. Empirically, we demonstrate the proposed formulation gives rise to reliable estimate of graph topology.

报告人简介:

Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012, respectively. He is an Associate Professor in the Department of Systems Engineering & Engineering Management at CUHK. He has held research positions at ASU, UC Davis, Telecom ParisTech, Ecole Polytechnique, MIT. He is an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks, IEEE Transactions on Signal Processing, and Elsevier’s Signal Processing. Hoi-To’s research interests are in the broad area of signal processing, machine learning and stochastic optimization. His dissertation has received the 2017’s Dean’s Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU and he is a recipient of Best Student Paper Awards at ICASSP 2018, SAM 2024 (as a co-author), ICASSP 2025 (as a co-author).