Dynamic stochastic blockmodels for time-evolving social networks

Abstract

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.

To appear in Journal of Selected Topics in Signal Processing special issue: Signal Processing for Social Networks

References in corpus (4)

• Stochastic blockmodels and community structure in networks
• Discrete Signal Processing on Graphs
• Graph Evolution: Densification and Shrinking Diameters
• Dynamic stochastic blockmodels: Statistical models for time-evolving networks

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