Dynamic Stochastic Blockmodels for Time-Evolving Social Networks

• Published in: IEEE journal of selected topics in signal processing Vol. 8; no. 4; pp. 552 - 562
• Main Authors: Xu, Kevin S., Hero, Alfred O.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation methods; Blocking; Covariance matrices; dynamic network; Dynamics; extended Kalman filter; Heuristic algorithms; Inference algorithms; Kalman filters; Markov analysis; Monte Carlo methods; Monte Carlo simulation; Networks; on-line estimation; Searching; Social networks; State-space social network model; Stochastic processes; Stochasticity; Vectors
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2014.2310294

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.