Estimation in a Binomial Stochastic Blockmodel for a Weighted Graph by a Variational Expectation Maximization Algorithm

Autor: Abir El Haj, Zaher Khraibani, Pierre-Yves Louis, Yousri Slaoui
Přispěvatelé: Laboratoire de Mathématiques et Applications (LMA-Poitiers), Université de Poitiers-Centre National de la Recherche Scientifique (CNRS), UL - Université Libanaise, Faculté des Sciences Section (1) Hadath-Beyrouth, Université Libanaise, Faculté des Sciences Section (1) Hadath-Beyrouth (UL)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Communications in Statistics-Simulation and Computation
Communications in Statistics-Simulation and Computation, Taylor & Francis, 2020, ⟨10.1080/03610918.2020.1743858⟩
ISSN: 0361-0918
1532-4141
DOI: 10.1080/03610918.2020.1743858⟩
Popis: International audience; Stochastic blockmodels have been widely proposed as a probabilistic random graph model for the analysis of networks data as well as for detecting community structure in these networks. In a number of real-world networks, not all ties among nodes have the same weight. Ties among networks nodes are often associated with weights that differentiate them in terms of their strength, intensity, or capacity. In this paper, we provide an inference method through a variational expectation maximization algorithm to estimate the parameters in binomial stochastic blockmodels for weighted networks. To prove the validity of the method and to highlight its main features, we set some applications of the proposed approach by using some simulated data and then some real data sets. Stochastic blockmodels belong to latent classes models. Classes defines a node's clustering. We compare the clustering found through binomial stochastic blockmodels with the ones found fitting a stochastic blockmodel with Poisson distributed edges. Inferred Poisson and binomial stochastic blockmodels mainly differs. Moreover, in our examples, the statistical error is lower for binomial stochastic blockmodels.
Databáze: OpenAIRE