Bayesian Analysis

Latent Space Approaches to Community Detection in Dynamic Networks

Daniel K. Sewell and Yuguo Chen

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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.

Article information

Source
Bayesian Anal. Volume 12, Number 2 (2017), 351-377.

Dates
First available in Project Euclid: 25 April 2016

Permanent link to this document
http://projecteuclid.org/euclid.ba/1461603847

Digital Object Identifier
doi:10.1214/16-BA1000

Keywords
clustering longitudinal data Markov chain Monte Carlo mixture model Pólya–Gamma distribution variational Bayes

Rights
Creative Commons Attribution 4.0 International License.

Citation

Sewell, Daniel K.; Chen, Yuguo. Latent Space Approaches to Community Detection in Dynamic Networks. Bayesian Anal. 12 (2017), no. 2, 351--377. doi:10.1214/16-BA1000. http://projecteuclid.org/euclid.ba/1461603847.


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