  Network data often take the form of repeated interactions between senders and
receivers tabulated over time. A primary question to ask of such data is which
traits and behaviors are predictive of interaction. To answer this question, a
model is introduced for treating directed interactions as a multivariate point
process: a Cox multiplicative intensity model using covariates that depend on
the history of the process. Consistency and asymptotic normality are proved for
the resulting partial-likelihood-based estimators under suitable regularity
conditions, and an efficient fitting procedure is described. Multicast
interactions--those involving a single sender but multiple receivers--are
treated explicitly. The resulting inferential framework is then employed to
model message sending behavior in a corporate e-mail network. The analysis
gives a precise quantification of which static shared traits and dynamic
network effects are predictive of message recipient selection.
