Latent Space Approaches to Community Detection in Dynamic Networks

Daniel K Sewell; Yuguo Chen

doi:10.1214/16-BA1000

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Latent Space Approaches to Community Detection in Dynamic Networks

Journal article

Open access

Peer reviewed

Latent Space Approaches to Community Detection in Dynamic Networks

Daniel K Sewell and Yuguo Chen

Bayesian analysis, Vol.12(2), pp.351-377

05/17/2020

DOI: 10.1214/16-BA1000

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https://doi.org/10.1214/16-BA1000View

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Abstract

Bayesian Anal. 12 (2017), no. 2, 351--377. https://projecteuclid.org/euclid.ba/1461603847 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.

Statistics - Methodology

Details

Title: Subtitle: Latent Space Approaches to Community Detection in Dynamic Networks
Creators: Daniel K Sewell
Yuguo Chen
Resource Type: Journal article
Publication Details: Bayesian analysis, Vol.12(2), pp.351-377
DOI: 10.1214/16-BA1000
ISSN: 1936-0975
eISSN: 1931-6690
Language: English
Date published: 05/17/2020
Academic Unit: Biostatistics; Public Policy Center (Archive)
Record Identifier: 9984214845102771

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