Paper Info
Title
Dynamic stochastic blockmodels: statistical models for time-evolving 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 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.
Year
DOI
Venue
2013
10.1007/978-3-642-37210-0_22
international conference on social computing
Keywords
DocType
Volume
social network,single time snapshot,aggregate view,dynamic network,time-evolving network,static network,statistical modeling,dynamic setting,state-space model,statistical model,dynamic stochastic blockmodels,dynamic social network,state space model
Conference
abs/1304.5974
ISSN
Citations 
PageRank 
Proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (2013) 201-210
17
0.84
References 
Authors
6
2
Name
Order
Citations
PageRank
Kevin S. Xu112213.49
Alfred O. Hero III22600301.12