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In this paper we explore previously unidentified connections betweenrelational event model (REM) from the field of network science and inversereinforcement learning (IRL) from the field of machine learning with respect totheir ability to characterize sequences of directed social interaction eventsin group settings. REM is a conventional approach to tackle such a problemwhereas the application of IRL is a largely unbeaten path. We begin byexamining the mathematical components of both REM and IRL and findstraightforward analogies between the two methods as well as uniquecharacteristics of the IRL approach. We demonstrate the special utility of IRLin characterizing group social interactions with an empirical experiment, inwhich we use IRL to infer individual behavioral preferences based on a sequenceof directed communication events from a group of virtual-reality game playersinteracting and cooperating to accomplish a shared goal. Our comparison andexperiment introduce fresh perspectives for social behavior analytics and helpinspire new research opportunities at the nexus of social network analysis andmachine learning.

Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences