A review of dynamic network models with latent variables

Bomin Kim, Kevin H. Lee, Lingzhou Xue, Xiaoyue Niu

Research output: Contribution to journalReview article

18 Scopus citations

Abstract

We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions. For each dynamic model, we also discuss its applications that have been studied in the literature, with the data source listed in Appendix. Based on the review, we summarize a list of open problems and challenges in dynamic network modeling with latent variables.

Original languageEnglish (US)
Pages (from-to)105-135
Number of pages31
JournalStatistics Surveys
Volume12
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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