Statistics Colloquium: Joshua Loyal, University Of Illinois At Urbana-Champaign

This event is part of the Spring 2022 Statistics Colloquium

An Eigenmodel for Dynamic Multilayer Networks

Presented by Joshua Loyal, Ph.D. Candidate, Department Of Statistics, University Of Illinois At Urbana-Champaign

Wednesday, January 5, 2022
4:00 p.m. ET

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 D. Loyal is a PhD candidate in the Department of Statistics at the University of Illinois at Urbana-Champaign advised by Professors Yuguo Chen and Ruoqing Zhu. He received an M.S. in Physics from Yale University and a B.S. in Physics and Mathematics from Duke University. 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