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June 2010 Strategies for online inference of model-based clustering in large and growing networks
Hugo Zanghi, Franck Picard, Vincent Miele, Christophe Ambroise
Ann. Appl. Stat. 4(2): 687-714 (June 2010). DOI: 10.1214/10-AOAS359

Abstract

In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.

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Hugo Zanghi. Franck Picard. Vincent Miele. Christophe Ambroise. "Strategies for online inference of model-based clustering in large and growing networks." Ann. Appl. Stat. 4 (2) 687 - 714, June 2010. https://doi.org/10.1214/10-AOAS359

Information

Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62096
MathSciNet: MR2758645
Digital Object Identifier: 10.1214/10-AOAS359

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 2 • June 2010
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