  Relational event data, which consist of events involving pairs of actors over
time, are now commonly available at the finest of temporal resolutions.
Existing continuous-time methods for modeling such data are based on point
processes and directly model interaction "contagion," whereby one interaction
increases the propensity of future interactions among actors, often as dictated
by some latent variable structure. In this article, we present an alternative
approach to using temporal-relational point process models for continuous-time
event data. We characterize interactions between a pair of actors as either
spurious or that resulting from an underlying, persistent connection in a
latent social network. We argue that consistent deviations from expected
behavior, rather than solely high frequency counts, are crucial for identifying
well-established underlying social relationships. This study aims to explore
these latent network structures in two contexts: one comprising of college
students and another involving barn swallows.
