Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques for network data at fine temporal granularity are crucial. This article makes two contributions to the field. First, we present statistical frameworks to simulate relational event networks under dyadic and actor-oriented relational event models implemented in an R package remulate. Second, we show how this simulation framework can address key challenges in temporal social network analysis through five case studies. The first study illustrates the necessity of simulation based techniques for model assessment, using a network of criminal gangs. The second shows how simulation supports social theory development which is illustrated via optimal distinctiveness theory. The third explores simulation for understanding the effects of network interventions. In the fourth study, we illustrate how simulation-based analysis can be used to assess the sensitivity of relational event models. The fifth study demonstrates how simulation frameworks can be used to make predictions about future relational dynamics. Through these case studies and software, researchers will be able to better understand social interaction dynamics using relational event data from real-life networks.
