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Activity Number: 319 - SLDS CSpeed 6
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #317793
Title: An Eigenmodel for Dynamic Multilayer Networks
Author(s): Joshua Daniel Loyal* and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: dynamic multilayer networks; epidemics on networks; latent space model; statistical network analysis; variational inference

Dynamic multilayer networks frequently represent the structure of multiple co-evolving relations; however, statistical models are not well-developed for this prevalent network type. Here, we propose a new latent space model for dynamic multilayer networks. The key feature of our model is its ability to identify common time-varying structures shared by all layers while also accounting for layer-wise variation and degree heterogeneity. We 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. We demonstrate the estimation procedure’s accuracy and scalability on simulated networks. We 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.

Authors who are presenting talks have a * after their name.

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