  We introduce the hyperedge event model (HEM)---a generative model for events
that can be represented as directed edges with one sender and one or more
receivers or one receiver and one or more senders. We integrate a dynamic
version of the exponential random graph model (ERGM) of edge structure with a
survival model for event timing to jointly understand who interacts with whom,
and when. The HEM offers three innovations with respect to the
literature---first, it extends a growing class of dynamic network models to
model hyperedges. The current state-of-the-art approach to dealing with
hyperedges is to inappropriately break them into separate edges/events. Second,
our model involves a novel receiver selection distribution that is based on
established edge formation models, but assures non-empty receiver lists. Third,
the HEM integrates separate, but interacting, equations governing edge
formation and event timing. We use the HEM to analyze emails sent among
department managers in Montgomery County government in North Carolina. Our
application demonstrates that the model is effective at predicting and
explaining time-stamped network data involving edges with multiple receivers.
We present an out-of-sample prediction experiment to illustrate how researchers
can select between different specifications of the model.
