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The modelling of networks using Exponential Random Graph Models: an introduction

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  • Johannes VAN DER POL

Abstract

Networks are representations of relational data. Whether the data used represents social interactions, cooperations or inter-bank dependencies, the structure of the network reflect a decision-making process based on many factors (common friends, technological proximity, geographical proximity). One of the objectives of network analysis is to identify these factors. The analysis of the structure using Exponential Random Graph Models (ERGM) can help in the identification of these factors by answering why two particular agents interact with one another, or why a specific agent has a particular position inside a network. In other words ERGMs allow us to perform an econometric analysis on network data. In the case of networks it is possible that a link depends upon the structure of the network. Usual econometric methods cannot be used because the dependence violates the hypothesis of independence of observations. ERGMs take into account this particularity of network data. \r\nThe aim of this paper is to present the statistical theory behind ERGM models and present an application using R-Project.

Suggested Citation

  • Johannes VAN DER POL, 2016. "The modelling of networks using Exponential Random Graph Models: an introduction," Cahiers du GREThA (2007-2019) 2016-22, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    • Handle: RePEc:grt:wpegrt:2016-22
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    File URL: http://cahiersdugretha.u-bordeaux.fr/2016/2016-22.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Network analysis ; ERGM ; Network structure ; R;
    All these keywords.

    JEL classification:

    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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