Work

Consistent Model Selection for the Degree Corrected Stochastic Blockmodel

Year: 2024

Type: article

Abstract: The Degree Corrected Stochastic Block Model (DCSBM) is a probabilistic model for random networks with community structure in which vertices of the same community are allowed to have distinct degree distributions.On the modeling side, this property makes the DCSBM more suitable for real-life complex networks.On the statistical side, it is more challenging due to a large number of parameters.In this paper, we prove that the penalized marginal likelihood estimator, when assuming prior distributions for the parameters, is strongly consistent for estimating the number of communities.We consider dense or semi-sparse random networks, and our estimator is unbounded, in the sense that the number of communities k considered can be as big as n, the number of nodes in the network.

Source: Latin American Journal of Probability and Mathematical Statistics

DOI: 10.30757/alea.v21-11

Authors: Andressa Cerqueira, Sandro Gallo, Florencia Leonardi, Cristel Vera

Institutions: Universidade Federal de São Carlos, Universidade de São Paulo

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico