A Review of Dynamic Network Models with Latent Variables

2017/11/13 by Bomin Kim, Kim, Bomin, Kevin Lee +5 · 6 citations
Computer Science · Physics and Astronomy · #Bayesian Methods and Mixture Models #Complex Network Analysis Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Opinion Dynamics and Social Influence #Other Statistics (stat.OT)

paper · pdf · doi:10.48550/arxiv.1711.10421

openalex publication_date 2017/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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