Networks and geography: Modelling community network structures as the outcome of both spatial and network processes

This paper focuses on how to extend the exponential random graph models to take into account the geographical embeddedness of individuals in modelling social networks. We develop a hierarchical set of nested models for spatially embedded social networks, in which, following Butts (2002), an interact...

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Bibliographic Details
Published in:Social networks 2012, Vol.34 (1), p.6-17
Main Authors: Daraganova, Galina, Pattison, Pip, Koskinen, Johan, Mitchell, Bill, Bill, Anthea, Watts, Martin, Baum, Scott
Format: Article
Language:English
Subjects:
Australia
Cluster analysis
Clustering
Distance
Endogenous network processes
Exponential random graph models
Geography
Networks
Organization
Roles
Social Networks
Spatial analysis
Spatial processes
Unemployment
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Summary:This paper focuses on how to extend the exponential random graph models to take into account the geographical embeddedness of individuals in modelling social networks. We develop a hierarchical set of nested models for spatially embedded social networks, in which, following Butts (2002), an interaction function between tie probability and Euclidean distance between nodes is introduced. The models are illustrated by an empirical example from a study of the role of social networks in understanding spatial clustering in unemployment in Australia. The analysis suggests that a spatial effect cannot solely explain the emergence of organised network structure and it is necessary to include both spatial and endogenous network effects in the model.
ISSN:0378-8733
1879-2111
DOI:10.1016/j.socnet.2010.12.001