Open Access
March 2023 A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks
Joshua Daniel Loyal, Yuguo Chen
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Bayesian Anal. 18(1): 49-77 (March 2023). DOI: 10.1214/21-BA1300


The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position cluster model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and demonstrate its ability to detect evolving community structure in a network of military alliances during the Cold War and a narrative network constructed from the Game of Thrones television series.

Funding Statement

This work was supported in part by a grant from Sandia National Laboratories.


The authors thank the associate editor and referees for their valuable comments and suggestions, which significantly improved this work.


Download Citation

Joshua Daniel Loyal. Yuguo Chen. "A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks." Bayesian Anal. 18 (1) 49 - 77, March 2023.


Published: March 2023
First available in Project Euclid: 8 February 2022

MathSciNet: MR4515725
Digital Object Identifier: 10.1214/21-BA1300

Keywords: longitudinal networks , mixture model , nonparametric Bayes , social networks , statistical network analysis

Vol.18 • No. 1 • March 2023
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