Dynamic stochastic blockmodels: Statistical models for time-evolving networks
This work addresses the need for statistical models to analyze time-evolving networks, such as social networks, which are more complex than static representations.
The authors tackled the problem of modeling dynamic networks, which are observed over multiple time points, by extending the static stochastic blockmodel to a state-space framework and fitting it with a modified extended Kalman filter. They applied this method to analyze a dynamic email communication network.
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.