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June 2017 Latent Space Approaches to Community Detection in Dynamic Networks
Daniel K. Sewell, Yuguo Chen
Bayesian Anal. 12(2): 351-377 (June 2017). DOI: 10.1214/16-BA1000

Abstract

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, this paper gives 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.

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Daniel K. Sewell. Yuguo Chen. "Latent Space Approaches to Community Detection in Dynamic Networks." Bayesian Anal. 12 (2) 351 - 377, June 2017. https://doi.org/10.1214/16-BA1000

Information

Published: June 2017
First available in Project Euclid: 25 April 2016

zbMATH: 1384.62203
MathSciNet: MR3620737
Digital Object Identifier: 10.1214/16-BA1000

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Vol.12 • No. 2 • June 2017
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