CSIS Seminar

An Eigenmodel for Dynamic Multilayer Networks

Abstract

Network (or graph) data is at the heart of many modern data science problems: disease transmission, community dynamics on social media, international relations, and others. In this talk, I will elaborate on my research in statistical inference for complex time-varying networks. I will focus on dynamic multilayer networks, which frequently represent the structure of multiple co-evolving relations. Despite their prevalence, statistical models are not well-developed for this network type. Here, I propose a new latent space model for dynamic multilayer networks. The key feature of this model is its ability to identify common time-varying structures shared by all layers while also accounting for layer-wise variation and degree heterogeneity. I establish the identifiability of the model's parameters and develop a structured mean-field variational inference approach to estimate the model's posterior, which scales to networks previously intractable to dynamic latent space models. I apply the model to two real-world problems: discerning regional conflicts in a data set of international relations and quantifying infectious disease spread throughout a school based on the student's daily contact patterns.

Speaker Bio

Joshua Loyal is a Ph.D. candidate in the Department of Statistics at the University of Illinois at Urbana-Champaign. He received an M.S. in Physics from Yale University and a B.S. in Physics and Mathematics from Duke University. In addition, from 2015 to 2018, he was a Data Scientist at DataRobot, a start-up in Boston aimed at building an automated machine learning platform. His research interests include statistical network analysis, Bayesian inference, machine learning, data science, and statistical computing. Join Zoom Meeting https://gmu.zoom.us/j/94093914005?pwd=c0NDQ1ZZRW9NS3B6UnlKcTJ2WlRKdz09