Dynamic stochastic blockmodels for time-evolving social networks
This work addresses the need for efficient statistical models for time-evolving social networks, representing an incremental improvement over existing dynamic network methods.
The authors tackled the problem of modeling dynamic networks observed over time by extending the static stochastic blockmodel to a state-space formulation, and they demonstrated that their extended Kalman filter-based algorithm performs competitively with state-of-the-art MCMC methods while being significantly less computationally demanding.
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.