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

• 成果类型: Article
• 署名作者: Zanghi, Hugo; Picard, Franck; Miele, Vincent; Ambroise, Christophe
• 署名单位: Dassault Systemes; VetAgro Sup; Universite Claude Bernard Lyon 1; INRAE; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/10-AOAS359
• 发表日期: 2010
• 页码: 687-714
• 关键词: mixed membership; em algorithm; mixture; CONVERGENCE; prediction

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.