  We assess the reliability of relational event model parameters estimated
under two sampling schemes: (1) uniform sampling from the observed events and
(2) case-control sampling which samples non-events, or null dyads ("controls"),
from a suitably defined risk set. We experimentally determine the variability
of estimated parameters as a function of the number of sampled events and
controls per event, respectively. Results suggest that relational event models
can be reliably fitted to networks with more than 12 million nodes connected by
more than 360 million dyadic events by analyzing a sample of some tens of
thousands of events and a small number of controls per event. Using data that
we collected on the Wikipedia editing network, we illustrate how network
effects commonly included in empirical studies based on relational event models
need widely different sample sizes to be estimated reliably. For our analysis
we use an open-source software which implements the two sampling schemes,
allowing analysts to fit and analyze relational event models to the same or
other data that may be collected in different empirical settings, varying
sample parameters or model specification.
