Bayesian approach to network modularity.
Phys Rev Lett
; 100(25): 258701, 2008 Jun 27.
Article
em En
| MEDLINE
| ID: mdl-18643711
ABSTRACT
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Phys Rev Lett
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos