  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 present a state-space model for dynamic networks that extends the
well-known stochastic blockmodel for static networks to the dynamic setting. We
fit the model in a near-optimal manner using an extended Kalman filter (EKF)
augmented with a local search. We demonstrate that the EKF-based algorithm
performs competitively with a state-of-the-art algorithm based on Markov chain
Monte Carlo sampling but is significantly less computationally demanding.
