Model-based clustering for populations of networks

作     者：Signorelli, Mirko Wit, Ernst C. 

作者机构：Leiden Univ Dept Biomed Data Sci Med Ctr Einthovenweg 20 NL-2333 ZC Leiden Netherlands Univ Svizzera Italiana Inst Computat Sci Lugano Switzerland Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Groningen Netherlands 

出 版 物：《STATISTICAL MODELLING》 (Stat. Model.)

年 卷 期：2020年第20卷第1期

页      面：9-29页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：COST Action European Cooperation for Statistics of Network Data Science [CA15109] 

主　　题：cognitive social structure EM algorithm Graph mixture of generalized linear models model-based clustering network modelling population of networks 

摘      要：Until recently obtaining data on populations of networks was typically rare. However, with the advancement of automatic monitoring devices and the growing social and scientific interest in networks, such data has become more widely available. From sociological experiments involving cognitive social structures to fMRI scans revealing large-scale brain networks of groups of patients, there is a growing awareness that we urgently need tools to analyse populations of networks and particularly to model the variation between networks due to covariates. We propose a model-based clustering method based on mixtures of generalized linear (mixed) models that can be employed to describe the joint distribution of a populations of networks in a parsimonious manner and to identify subpopulations of networks that share certain topological properties of interest (degree distribution, community structure, effect of covariates on the presence of an edge, etc.). Maximum likelihood estimation for the proposed model can be efficiently carried out with an implementation of the EM algorithm. We assess the performance of this method on simulated data and conclude with an example application on advice networks in a small business.