Dynamic relational event modeling: Testing, exploring, and applying.

ABSTRACT: The relational event model (REM) facilitates the study of network evolution in relational event history data, i.e., time-ordered sequences of social interactions. In real-life social networks it is likely that network effects, i.e., the parameters that quantify the relative importance of drivers of these social interaction sequences, change over time. In these networks, the basic REM is not appropriate to understand what drives network evolution. This research extends the REM framework with approaches for testing and exploring time-varying network effects. First, we develop a Bayesian approach to test whether network effects change during the study period. We conduct a simulation study that illustrates that the Bayesian test accurately quantifies the evidence between a basic ('static') REM

SUBMITTER: Meijerink-Bosman M 

PROVIDER: S-EPMC9342787 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature