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Strategies for online inference of model-based clustering in large and growing networks

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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https://hal.archives-ouvertes.fr/hal-00539318
Contributor : Stéphane Delmotte Connect in order to contact the contributor
Submitted on : Wednesday, November 24, 2010 - 3:19:56 PM
Last modification on : Monday, April 25, 2022 - 5:47:59 PM

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H. Zanghi, Franck Picard, V. Miele, C. Ambroise. Strategies for online inference of model-based clustering in large and growing networks. Annals of Applied Statistics, Institute of Mathematical Statistics, 2010, 4 (2), pp.687-714. ⟨10.1214/10-AOAS359⟩. ⟨hal-00539318⟩

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