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Exponential random graph models for multilevel networks

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Date
2013-01
Dewey
Sociologie économique
Sujet
Graphes, Théorie des; Modèles mathématiques; Exponential random graph models; Multilevel networks; Réseaux sociaux
Journal issue
Social Networks
Volume
35
Number
1
Publication date
01-2013
Article pages
96–115
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.socnet.2013.01.004
URI
https://basepub.dauphine.fr/handle/123456789/11051
Collections
  • IRISSO : Publications
Metadata
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Author
Pattison, Philippa
Robins, Garry
Wang, Peng
Lazega, Emmanuel
Type
Article accepté pour publication ou publié
Abstract (EN)
Modern 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.

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