Models of dynamic networks—networks that evolve over time—have manifold applications.
We develop a discrete time generative model for social network evolution that inherits the
richness and flexibility of the class of exponential family random-graph models. The model—a
separable temporal exponential family random-graph model—facilitates separable modelling of
the tie duration distributions and the structural dynamics of tie formation.We develop likelihoodbased
inference for the model and provide computational algorithms for maximum likelihood
estimation.We illustrate the interpretability of the model in analysing a longitudinal network of
friendship ties within a school.