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

Johannes van der Pol

Cahiers du GREThA (2007-2019) from Groupe de Recherche en Economie Théorique et Appliquée (GREThA)

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.

Keywords: Network analysis; ERGM; Network structure; R (search for similar items in EconPapers)
JEL-codes: C13 C59 D85 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-net and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:grt:wpegrt:2016-22

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