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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Bibliographic Details
Published in:	Journal of computational and graphical statistics Vol. 28; no. 3; pp. 609 - 619
Main Authors:	Roy, Sandipan, Atchadé, Yves, Michailidis, George
Format:	Journal Article
Language:	English
Published:	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
Subjects:	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
Online Access:	Get full text
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