Stochastic block models have known a flowering interest in the social network literature. They provide a tool for discovering communities and identifying clusters of individuals characterized by similar social behaviors. In this framework, full maximum likelihood estimates are not achievable due to the intractability of the likelihood function. For this reason, several approximate solutions are available in the literature. In this respect, a new and more efficient approximate method for estimating model parameters is introduced. This has a hybrid nature, in the sense that it exploits different features of existing methods. The proposal is illustrated by an intensive Monte Carlo simulation study and an application to a real-world network. (C) 2022 Elsevier B.V. All rights reserved.

Hybrid maximum likelihood inference for stochastic block models

Maria Francesca Marino;Silvia Pandolfi
2022

Abstract

Stochastic block models have known a flowering interest in the social network literature. They provide a tool for discovering communities and identifying clusters of individuals characterized by similar social behaviors. In this framework, full maximum likelihood estimates are not achievable due to the intractability of the likelihood function. For this reason, several approximate solutions are available in the literature. In this respect, a new and more efficient approximate method for estimating model parameters is introduced. This has a hybrid nature, in the sense that it exploits different features of existing methods. The proposal is illustrated by an intensive Monte Carlo simulation study and an application to a real-world network. (C) 2022 Elsevier B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1532973
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