Dynamic degree-corrected blockmodels for social networks: A nonparametric approach. (August 2019)

• Record Type: Journal Article
• Authors: Tan, Linda SL; Iorio, Maria De
• Is Part Of: Statistical modelling. Volume 19:Number 4(2019)
• Page Start: 386
• Page End: 411
• Publication Date: 2019-08
• Subjects: Community detection -- degree correction -- Dirichlet process -- stochastic blockmodels; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18770760
• Languages: English
• ISSNs: 1471-082X

Abstract

A nonparametric approach to the modelling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation, and popularity parameters are incorporated to account for degree heterogeneity. We specify a Dirichlet process prior to detect community structure as well as to induce clustering in the popularity parameters. This approach is flexible yet parsimonious as it allows the appropriate number of communities and popularity clusters to be determined automatically by the data. We further discuss and implement extensions of the static model to dynamic networks. In a Bayesian framework, we perform posterior inference through MCMC algorithms. The models are illustrated using several real-world benchmark social networks.