Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project

Johannes van der Pol

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Abstract: Exponential family random graph models (ERGM) are increasingly used in the study of social networks. These models are build to explain the global structure of a network while allowing inference on tie prediction on a micro level. The number of papers within economics is however limited. Possible applications for economics are however abundant. The aim of this document is to provide an explanation of the basic mechanics behind the models and provide a sample code (using R and the packages statnet and ERGM) to operationalize and interpret results and analyse goodness of fit. After reading this paper the reader should be able to start their own analysis.

Keywords: Networks; Exponential random graph model (ERGM); Innovation networks; p-Star (p*); Statnet; Tie formation (search for similar items in EconPapers)
Date: 2018
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Published in Computational Economics, inPress, 54 (3), pp.845-875. ⟨10.1007/s10614-018-9853-2⟩

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Working Paper: Introduction to network modeling using Exponential Random Graph Models (ERGM): Theory and an application using R-project (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02486328

DOI: 10.1007/s10614-018-9853-2

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