期刊
SOCIAL NETWORKS
卷 34, 期 1, 页码 6-17出版社
ELSEVIER
DOI: 10.1016/j.socnet.2010.12.001
关键词
Exponential random graph models; Endogenous network processes; Clustering; Distance; Spatial 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 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. (C) 2010 Elsevier B.V. All rights reserved.
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