  Significant efforts have gone into the development of statistical models for
analyzing data in the form of networks, such as social networks. Most existing
work has focused on modeling static networks, which represent either a single
time snapshot or an aggregate view over time. There has been recent interest in
statistical modeling of dynamic networks, which are observed at multiple points
in time and offer a richer representation of many complex phenomena. In this
paper, we propose a state-space model for dynamic networks that extends the
well-known stochastic blockmodel for static networks to the dynamic setting. We
then propose a procedure to fit the model using a modification of the extended
Kalman filter augmented with a local search. We apply the procedure to analyze
a dynamic social network of email communication.
