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arXiv:2403.19329v1 Announce Type: cross
Abstract: Many important social phenomena result from repeated interactions among individuals over time such as email exchanges in an organization, or face-to-face interactions in a classroom. Insights into the mechanisms underlying the dynamics of these interactions can be achieved through simulations of networks on a fine temporal granularity. In this paper, we present statistical frameworks to simulate relational event networks under dyadic and actor-oriented relational event models. These simulators have a broad applicability in temporal social network research such as model fit assessment, theory building, network intervention planning, making predictions, understanding the impact of network structures, to name a few. We show this in three extensive applications. First, it is shown why simulation-based techniques are crucial for relational event model assessment, for example to investigate how past events affect future interactions in the network. Second, we demonstrate how simulation techniques contribute to a better understanding of the longevity of network interventions. Third, we show how simulation techniques are important when building and extending theories about social phenomena such as understanding social identity dynamics using optimal distinctiveness theory.