A multilayer exponential random graph modelling approach for weighted networks

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Title: A multilayer exponential random graph modelling approach for weighted networks
Authors: Caimo, AlbertoGollini, Isabella
Permanent link: http://hdl.handle.net/10197/10992
Date: 14-Aug-2019
Online since: 2019-08-19T10:21:23Z
Abstract: A new modelling approach for the analysis of weighted networks with ordinal/polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.
Type of material: Journal Article
Publisher: Elsevier BV
Journal: Computational Statistics & Data Analysis
Volume: 142
Copyright (published version): 2019 Elsevier
Keywords: Statistical network modelsWeighted networksBayesian analysisIntractable models
DOI: 10.1016/j.csda.2019.106825
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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