Show simple item record

dc.contributor.authorPattison, Philippa
dc.contributor.authorRobins, Garry
dc.contributor.authorWang, Peng
dc.contributor.authorLazega, Emmanuel
dc.date.accessioned2013-02-26T08:33:42Z
dc.date.available2013-02-26T08:33:42Z
dc.date.issued2013-01
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11051
dc.language.isoenen
dc.subjectGraphes, Théorie desen
dc.subjectModèles mathématiquesen
dc.subjectExponential random graph modelsen
dc.subjectMultilevel networksen
dc.subjectRéseaux sociauxen
dc.subject.ddc306.3en
dc.titleExponential random graph models for multilevel networksen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherMelbourne School of Psychological Sciences;Australie
dc.contributor.editoruniversityotherMelbourne School of Psychological Sciences;Australie
dc.contributor.editoruniversityotherMelbourne School of Psychological Sciences;Australie
dc.description.abstractenModern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (Lazega et al., 2006 and Lazega et al., 2008) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.en
dc.relation.isversionofjnlnameSocial Networks
dc.relation.isversionofjnlvol35
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2013-01
dc.relation.isversionofjnlpages96–115
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.socnet.2013.01.004en
dc.relation.isversionofjnlpublisherElsevier
dc.subject.ddclabelSociologie économiqueen
dc.relation.forthcomingnonen


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record