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An introduction to exponential random graph (p*) models for social networks

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Robins, Garry, Pattison, Pip, Kalish, Yuval, Lusher, Dean·2007·Social Networks
DOI 10.1016/j.socnet.2006.08.002
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Abstract

This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph

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Research works printed but not linked: 32

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Metadata

Authors
Robins, G., Pattison, P., Kalish, Y., & Lusher, D.
Venue
Social Networks
Publisher
Elsevier BV
Published
2007-05
Volume
29
Issue
2
Pages
173-191
ISSN
0378-8733 (ISSN-L)
DOI
10.1016/j.socnet.2006.08.002
OpenAlex
W2160268549
Cited by
1,995
References
0
FWCI
17.35
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks. https://doi.org/10.1016/j.socnet.2006.08.002

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.