Parameterisation of Keeling's network generation algorithm

Jennifer Badham*, Hussein Abbass, Rob Stocker

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

Original languageEnglish
Pages (from-to)161-166
Number of pages6
JournalTheoretical Population Biology
Volume74
Issue number2
DOIs
Publication statusPublished - Sep 2008
Externally publishedYes

Keywords

  • Assortativity
  • Clustering
  • Disease spread
  • Social networks
  • Transmission networks

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Ecology, Evolution, Behavior and Systematics

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