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Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time

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  • Cornelius Fritz
  • Michael Lebacher
  • Göran Kauermann

Abstract

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie‐oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the temporal exponential random graph model (TERGM) and the separable TERGM (STERGM), both being time‐discrete models. These models are then contrasted with continuous process models, focusing on the relational event model (REM). We additionally show how the REM can handle time‐clustered observations, that is, continuous‐time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange, respectively. The data allow to demonstrate the applicability and interpretation of the network models.

Suggested Citation

  • Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:3:p:275-299
    DOI: 10.1111/stan.12198
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    5. Antonio Mario Arrizza & Alberto Caimo, 2021. "Bayesian dynamic network actor models with application to South Korean COVID-19 patient movement data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1465-1483, December.

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