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

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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.

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Notes

  1. The values 0 and 1 refer to values found in an adjacency matrix, 1 indicating the presence of a link, 0 the absence.

  2. This type of model is also referred to as the \(P^*\) family of models (Anderson et al. 1999; Lusher et al. 2012).

  3. Nearest-neighbor systems have been studied by Besag (1972).

  4. In addition, the degeneracy of the model can result in problems with the estimation procedures.

  5. In the algorithm the initial values are chosen to be the MPLE.

  6. \(e^{0.7575}\) since this is the odds and not the log-odds.

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Authors and Affiliations

  1. VIA Inno, GREThA UMR-CNRS 5113, University of Bordeaux, Avenue Léon Duguit, 33608, Pessac, France

    Johannes van der Pol

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  1. Johannes van der Pol
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Correspondence to Johannes van der Pol.

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Financial support from IdEx Bordeaux and ITEMM-Lab as well as valuable comments and suggestions from Murat Yildizoglu and several anonymous reviewers are gratefully acknowledged.

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van der Pol, J. Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project. Comput Econ 54, 845–875 (2019). https://doi.org/10.1007/s10614-018-9853-2

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