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 or a dynamic REM. Secondly, in the case of the latter, time-varying network effects can be studied by means of a moving window that slides over the relational event history. A simulation study was conducted that illustrates that the accuracy and precision of the estimates depend on the window width: narrower windows result in greater accuracy at the cost of lower precision. Third, we develop a Bayesian approach for determining window widths using the empirical network data and conduct a simulation study that illustrates that estimation with empirically determined window widths achieves both good accuracy for time intervals with important changes and good precision for time intervals with hardly any changes in the effects. Finally, in an empirical application, we illustrate how the approaches in this research can be used to test for and explore time-varying network effects of face-to-face contacts at the workplace.

Collective intelligence in teams: Contextualizing collective intelligent behavior over time

“…We briefly highlight a few other recent developments for the analysis of high-resolution time-stamped interaction data. First, we suggest relational event models as uniquely suitable as they have been developed to analyze time-stamped (or ordered, without the precise time-stamp) interaction patterns across members of a team ( Quintane et al, 2014 ; Leenders et al, 2016 ; Pilny et al, 2016 ; Schecter et al, 2018 ; Mulder and Leenders, 2019 ; Meijerink-Bosman et al, 2022b ). Relational event models are built on a simple idea: the rate at which two individuals interact at a specific point in time is determined by past team interactions.…”