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Science and Mathematics

Statistical Inference for Complex Dynamic Networks

December 16, 2021 at 4:00pm5:00pm EST

Virtual (See event details)

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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 

This event was published on December 10, 2021.


Event Details