Statistical Inference for Complex Dynamic Networks

The Department of Mathematics in the College of Arts and Sciences is honored to welcome Mr. Joshua Loyal to deliver the weekly colloquium. Mr. Loyal is a PhD student in Statistics at the University of Illinois at Urbana-Champaign, where he works in statistical network analysis, Bayesian inference, machine learning, data science, and statistical computing.

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.

Contact Leah Quinones at Laquinon@syr.edu for Zoom link