STRATEGIES FOR ONLINE INFERENCE OF MODEL-BASED CLUSTERING IN LARGE AND GROWING NETWORKS

被引：19

作者：

Zanghi, Hugo ^{[1
]}

Picard, Franck ^{[2
]}

Miele, Vincent ^{[2
]}

Ambroise, Christophe ^{[3
]}

机构：

[1] Exalead, F-75008 Paris, France

[2] UCB Lyon 1, Lab Biometrie & Biol Evolut, F-69622 Villeurbanne, France

[3] CNRS, INRA, Lab Stat & Genome, UEVE 1152,UMR 8071, F-91000 Evry, France

来源：

ANNALS OF APPLIED STATISTICS | 2010年 / 4卷 / 02期

关键词：

Graph clustering; EM Algorithms; online strategies; web graph structure analysis; MIXED MEMBERSHIP; EM ALGORITHM; MIXTURE; CONVERGENCE; PREDICTION;

D O I：

10.1214/10-AOAS359

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：687 / 714

页数：28

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