A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs

Carter T. Butts, Zack W. Almquist

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The conventional exponential family random graph model (ERGM) parameterization leads to a baseline density that is constant in graph order (i.e., number of nodes); this is potentially problematic when modeling multiple networks of varying order. Prior work has suggested a simple alternative that results in constant expected mean degree. Here, we extend this approach by suggesting another alternative parameterization that allows for flexible modeling of scenarios in which baseline expected degree scales as an arbitrary power of order. This parameterization is easily implemented by the inclusion of an edge count/log order statistic along with the traditional edge count statistic in the model specification.

Original languageEnglish (US)
Pages (from-to)163-167
Number of pages5
JournalJournal of Mathematical Sociology
Volume39
Issue number3
DOIs
StatePublished - Jul 3 2015

Bibliographical note

Funding Information:
This work is based on research supported by National Science Foundation award #IIS-1251267 and Army Research Office award #W911NF-14-1-0552.

Publisher Copyright:
© 2015, Copyright © Taylor & Francis Group, LLC.

Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.

Keywords

  • baseline models
  • exponential family random graph models (ERGMs)
  • mean degree
  • model parameterization

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