  Modeling of dynamic networks -- networks that evolve over time -- has
manifold applications in many fields. In epidemiology in particular, there is a
need for data-driven modeling of human sexual relationship networks for the
purpose of modeling and simulation of the spread of sexually transmitted
disease. Dynamic network data about such networks are extremely difficult to
collect, however, and much more readily available are egocentrically sampled
data of a network at a single time point, with some attendant information about
the sexual history of respondents.
  Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM)
framework, which facilitates separable modeling of the tie duration
distributions and the structural dynamics of tie formation. In this work, we
apply this modeling framework to this problem, by studying the long-run
properties of STERGM processes, developing methods for fitting STERGMs to
egocentrically sampled data, and extending the network size adjustment method
of Krivitsky, Handcock, and Morris (2011) to dynamic models.
