  Recent developments in computers and automated data collection strategies
have greatly increased the interest in statistical modeling of dynamic
networks. Many of the statistical models employed for inference on large-scale
dynamic networks suffer from limited forward simulation/prediction ability. A
major problem with many of the forward simulation procedures is the tendency
for the model to become degenerate in only a few time steps, i.e., the
simulation/prediction procedure results in either null graphs or complete
graphs. Here, we describe an algorithm for simulating a sequence of networks
generated from lagged dynamic network regression models DNR(V), a sub-family of
TERGMs. We introduce a smoothed estimator for forward prediction based on
smoothing of the change statistics obtained for a dynamic network regression
model. We focus on the implementation of the algorithm, providing a series of
motivating examples with comparisons to dynamic network models from the
literature. We find that our algorithm significantly improves multi-step
prediction/simulation over standard DNR(V) forecasting. Furthermore, we show
that our method performs comparably to existing more complex dynamic network
analysis frameworks (SAOM and STERGMs) for small networks over short time
periods, and significantly outperforms these approaches over long time time
intervals and/or large networks.
