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Marginalized exponential random graph models

Journal Article


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Abstract


  • Exponential random graph models (ERGMs) are a popular tool for modeling social

    networks representing relational data, such as working relationships or friendships.

    Data on exogenous variables relating to participants in the network, such as gender or

    age, are also often collected. ERGMs allow modeling of the effects of such exogenous

    variables on the joint distribution, specified by the ERGM, but not on the marginal

    probabilities of observing a relationship. In this article, we consider an approach to

    modeling a network that uses an ERGM for the joint distribution of the network, but

    then marginally constrains the fit to agree with a generalized linear model (GLM)

    defined in terms of this set of exogenous variables. This type of model, which we refer

    to as a marginalized ERGM, is a natural extension of the standard ERGM that allows

    a convenient population-averaged interpretation of parameters, for example, in terms

    of log odds ratios when the GLM includes a logistic link, as well as fast computation

    of marginal probabilities. Several algorithms to obtain maximum likelihood estimates

    are presented, with a particular focus on reducing the computational burden. These

    methods are illustrated using data on the working relationship between 36 partners in a

    New England law firm.

Publication Date


  • 2012

Citation


  • Suesse, T. F. (2012). Marginalized exponential random graph models. Journal of Computational and Graphical Statistics, 21 (4), 883-900.

Scopus Eid


  • 2-s2.0-84883289481

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1135&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/130

Number Of Pages


  • 17

Start Page


  • 883

End Page


  • 900

Volume


  • 21

Issue


  • 4

Abstract


  • Exponential random graph models (ERGMs) are a popular tool for modeling social

    networks representing relational data, such as working relationships or friendships.

    Data on exogenous variables relating to participants in the network, such as gender or

    age, are also often collected. ERGMs allow modeling of the effects of such exogenous

    variables on the joint distribution, specified by the ERGM, but not on the marginal

    probabilities of observing a relationship. In this article, we consider an approach to

    modeling a network that uses an ERGM for the joint distribution of the network, but

    then marginally constrains the fit to agree with a generalized linear model (GLM)

    defined in terms of this set of exogenous variables. This type of model, which we refer

    to as a marginalized ERGM, is a natural extension of the standard ERGM that allows

    a convenient population-averaged interpretation of parameters, for example, in terms

    of log odds ratios when the GLM includes a logistic link, as well as fast computation

    of marginal probabilities. Several algorithms to obtain maximum likelihood estimates

    are presented, with a particular focus on reducing the computational burden. These

    methods are illustrated using data on the working relationship between 36 partners in a

    New England law firm.

Publication Date


  • 2012

Citation


  • Suesse, T. F. (2012). Marginalized exponential random graph models. Journal of Computational and Graphical Statistics, 21 (4), 883-900.

Scopus Eid


  • 2-s2.0-84883289481

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1135&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/130

Number Of Pages


  • 17

Start Page


  • 883

End Page


  • 900

Volume


  • 21

Issue


  • 4