  We present a statistical framework for generating predicted dynamic networks
based on the observed evolution of social relationships in a population. The
framework includes a novel and flexible procedure to sample dynamic networks
given a probability distribution on evolving network properties; it permits the
use of a broad class of approaches to model trends, seasonal variability,
uncertainty, and changes in population composition. Current methods do not
account for the variability in the observed historical networks when predicting
the network structure; the proposed method provides a principled approach to
incorporate uncertainty in prediction. This advance aids in the designing of
network-based interventions, as development of such interventions often
requires prediction of the network structure in the presence and absence of the
intervention. Two simulation studies are conducted to demonstrate the
usefulness of generating predicted networks when designing network-based
interventions. The framework is also illustrated by investigating results of
potential interventions on bill passage rates using a dynamic network that
represents the sponsor/co-sponsor relationships among senators derived from
bills introduced in the US Senate from 2003-2016.
