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Activity Number: 613 - Recent Advances in Network Data Inference
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #329047 Presentation
Title: Latent Space Approaches to Community Detection in Dynamic Networks
Author(s): Yuguo Chen* and Daniel Sewell
Companies: University of Illinois at Urbana-Champaign and University of Iowa
Keywords: Clustering; Longitudinal data; Markov chain Monte Carlo; Mixture model; Polya-Gamma distribution; Variational Bayes

Embedding dyadic data into a latent space has long been a popular approach to modeling networks of all kinds. While clustering has been done using this approach for static networks, we give two methods of community detection within dynamic network data, building upon the distance and projection models previously proposed in the literature. Our proposed approaches capture the time-varying aspect of the data, can model directed or undirected edges, inherently incorporate transitivity and account for each actor's individual propensity to form edges. We provide Bayesian estimation algorithms, and apply these methods to a ranked dynamic friendship network and world export/import data.

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

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