A mixture model for random graphs

Jean-Jacques Daudin 1 Franck Picard 1 Stéphane Robin 1
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : {The Erdos-Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-word networks. The vertices of these networks are often structured in \textit{prior} unknown clusters (functionally related proteins or social communities) with different connectivity properties. We define a generalization of the Erdos-Rényi model called ERMG for Erdos-Rényi Mixtures for Graphs. This new model is based on mixture distributions. We give some of its properties, an algorithm to estimate its parameters and apply this method to uncover the modular structure of a network of enzymatic reactions.
Type de document :
[Research Report] RR-5840, INRIA. 2006, pp.19
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Contributeur : Rapport de Recherche Inria <>
Soumis le : vendredi 19 mai 2006 - 19:25:52
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
Document(s) archivé(s) le : dimanche 4 avril 2010 - 20:28:17



  • HAL Id : inria-00070186, version 1



Jean-Jacques Daudin, Franck Picard, Stéphane Robin. A mixture model for random graphs. [Research Report] RR-5840, INRIA. 2006, pp.19. 〈inria-00070186〉



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