MixNet


Package based on a new probabilistic model for random graphs . This model is based on the hypothesis that real networks are made of classes which show specific connectivity patterns. 

The Erdös–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-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.


Informations générales
Partenaire externe
CNRS / UEVE
Informations spécifiques
Langage(s) de développement
C; C++
N° de version courante
V1.1.2-p1
Date de la version courante
OS supporté
Type de licence


Porteur(s)
Auteur(s)
Daudin J.-J.
Robin S.
Picard F.
Contact
stephane.robin@inra.fr
Publication de référence


 

 

Système d'information scientifique MIA classé par unité (UR, UMR)

Logo BIOSP     Logo MISTEA     Logo MIA Toulouse     Logo MaIAGE     Logo MIA Paris