Statistical inference for valued-edge networks: The generalized exponential random graph model

Bruce A. Desmarais, Skyler J. Cranmer

Research output: Contribution to journalArticle

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Abstract

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.

Original languageEnglish (US)
Article numbere30136
JournalPloS one
Volume7
Issue number1
DOIs
StatePublished - Jan 19 2012

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All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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