A multilayer exponential random graph modelling approach for weighted networks

DC FieldValueLanguage
dc.contributor.authorCaimo, Alberto-
dc.contributor.authorGollini, Isabella-
dc.date.accessioned2019-08-19T10:21:23Z-
dc.date.available2019-08-19T10:21:23Z-
dc.date.copyright2019 Elsevieren_US
dc.date.issued2019-08-14-
dc.identifier.citationComputational Statistics & Data Analysisen_US
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/10197/10992-
dc.description.abstractA 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.en_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rightsThis is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis (142, (2019)) https://doi.org/10.1016/j.csda.2019.106825en_US
dc.subjectStatistical network modelsen_US
dc.subjectWeighted networksen_US
dc.subjectBayesian analysisen_US
dc.subjectIntractable modelsen_US
dc.titleA multilayer exponential random graph modelling approach for weighted networksen_US
dc.typeJournal Articleen_US
dc.internal.authorcontactotherisabella.gollini@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume142en_US
dc.citation.otherArticle number: 106825en_US
dc.check.date2022-04-19-
dc.identifier.doi10.1016/j.csda.2019.106825-
dc.neeo.contributorCaimo|Alberto|aut|-
dc.neeo.contributorGollini|Isabella|aut|-
dc.date.embargo2021-08-14en_US
dc.description.admin24 month embargo - ACen_US
dc.description.adminUpdate issue date during checkdate report - ACen_US
dc.date.updated2019-08-15T09:35:03Z-
item.fulltextWith Fulltext-
item.grantfulltextembargo_20210814-
Appears in Collections:Mathematics and Statistics Research Collection
Files in This Item:
Access to this item has been restricted by the copyright holder until:2021-08-14
File Description SizeFormat 
CSDA_2019.pdf3.76 MBAdobe PDF    Request a copy
Show simple item record

Page view(s)

297
Last Week
5
Last month
16
checked on Aug 11, 2020

Download(s)

76
checked on Aug 11, 2020

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.