Latent space approaches to community detection in dynamic networks

Daniel K. Sewell, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)351-377
Number of pages27
JournalBayesian Analysis
Issue number2
StatePublished - Jun 1 2017


  • Clustering
  • Longitudinal data
  • Markov chain monte carlo
  • Mixture model
  • Pólya-gamma distribution
  • Variational bayes

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


Dive into the research topics of 'Latent space approaches to community detection in dynamic networks'. Together they form a unique fingerprint.

Cite this