Relational hyperevent models for polyadic interaction networks

Polyadic, or "multicast" social interaction networks arise when one sender addresses multiple receivers simultaneously. Currently available relational event models (REM) are not well suited to the analysis of polyadic because they specify rates for sets as functions dyadic covariates associated with and receiver at a time. Relational hyperevent (RHEM) address this problem by specifying hyperedge entire set receivers. For instance, can express tendency senders repeatedly same pairs (or larger sets) - simple frequent pattern in data which, however, cannot be expressed covariates. In article we demonstrate potential benefits RHEMs interaction. We define discuss practically relevant effects that REMs but may incorporated empirical specifications RHEM. illustrate value RHEM, compare them related REM, reanalysis canonical Enron email data.