Statistical clustering of temporal networks through a dynamic stochastic block model

Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 79; H. 4; S. 1119 - 1141
Hauptverfasser:	Matias, Catherine, Miele, Vincent
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Wiley 01.09.2017 Oxford University Press Royal Statistical Society
Schlagworte:	algorithms animals Binary data Clustering Contact network data collection Discrete time Dynamic random graph equations Graph clustering Graphs Group dynamics Groups high school students Inference Markov analysis Markov chain Markov chains Mathematics Networks Parameter identification Property Regression analysis Secondary school students Secondary schools Statistical inference Statistical methods Statistics Stochastic block model Stochastic models Switching theory Time Variational expectation–maximization stochastic block model variational expectation maximization contact network dynamic random graph graph clustering
ISSN:	1369-7412, 1467-9868
Online-Zugang:	Volltext
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