Relational hyperevent models for polyadic interaction networks
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Polyadic (one-to-many) social interaction happens when one sender addresses multiple receivers simultaneously. Currently available relational event models (REM) are not well suited to the analysis of polyadic interaction networks because they specify event rates for sets of receivers as functions of dyadic covariates associated with the sender and one receiver at a time. Relational hyperevent models (RHEM) alleviate this problem by specifying event rates as functions of hyperedge covariates associated with the sender and the entire set of receivers. In this article we demonstrate the potential benefits of RHEMs for the analysis of polyadic social interaction. We define and implement practically relevant effects that are not available for REMs but may be incorporated in empirical specifications of RHEM. In a reanalysis of the canonical Enron email data, we illustrate how RHEMs effectively (i) reveal evidence of polyadic dependencies in empirical data, (ii) improve the fit over comparable dyadic specifications of REMs, and (iii) better identify the set of recipients actually receiving the same email message from sets of potential recipients who could have received the same email message, but did not.
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