Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication

We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-...

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Vydané v:	Journal of computational and graphical statistics Ročník 28; číslo 3; s. 609 - 619
Hlavní autori:	Roy, Sandipan, Atchadé, Yves, Michailidis, George
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Taylor & Francis 03.07.2019 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd
Predmet:	Algorithms Case-control approximation Computer simulation Iterative methods Mathematical models Maximum likelihood estimates Monte Carlo EM Multivariate, Multilevel, and Mixture Modelling Parallel computation with communication Parallel processing Parameter estimation Social network Social networks Subsampling social network case-control approximation Monte-Carlo EM parallel computation with communication subsampling
ISSN:	1061-8600, 1537-2715
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