  Generative models for network time series (also known as dynamic graphs) have
tremendous potential in fields such as epidemiology, biology and economics,
where complex graph-based dynamics are core objects of study. Designing
flexible and scalable generative models is a very challenging task due to the
high dimensionality of the data, as well as the need to represent temporal
dependencies and marginal network structure. Here we introduce DAMNETS, a
scalable deep generative model for network time series. DAMNETS outperforms
competing methods on all of our measures of sample quality, over both real and
synthetic data sets.
