Scalable Durational Event Models: Application to Physical and Digital Interactions

Abstract

Durable interactions are increasingly observed in social network analysis with precise timestamps. Phone and video calls, for instance, are events to which a specific duration can be assigned. We term data encoding interactions with start and end times ``durational event data''. Recent advances in data collection have enabled the observation of such data over extended periods and across large populations of actors. Methodologically, we propose the Durational Event Model, an extension of Relational Event Models that decouples the modeling of event incidence from event duration. Computationally, we derive a fast, memory-efficient, and exact block-coordinate ascent algorithm to facilitate large-scale inference. Theoretical complexity analysis and numerical simulations demonstrate the computational superiority of this approach over state-of-the-art methods. We apply the model implemented in the R package redeem to physical and digital interactions among college students in Copenhagen. Our empirical findings reveal that past interactions drive physical interactions, whereas digital interactions are influenced predominantly by friendship ties and prior dyadic contact.