Estudo comparativo de métodos de seleção de modelos para redes aleatórias

Abstract

In recent years, complex networks have been widely used to model systems with objects of interest, called vertices, which interact with each other by a haracteristic or relationship, represented by an edge. Through the various uses of complex networks, community detection has a great importance in the field, aiming to group vertices into the same cluster based on a given similarity between them. Thus, several models have been proposed to perform the clustering, assuming the number of communities is known previously, but this assumption is not always acceptable in real-world data. Therefore, in this work, we address the case of an unknown number of communities, that is a model selection problem, where a study has been conducted to compare selection methods for the Stochastic Block Model, including proposed methods based on normalized maximum likelihood and computationally approximated