A spatial approach to network generation for three properties: Degree distribution, clustering coefficient and degree assortativity

Jennifer Badham*, Rob Stocker

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.

Original languageEnglish
JournalJournal of Artificial Societies and Social Simulation
Volume13
Issue number1
Publication statusPublished - Jan 2010

Keywords

  • Assortativity
  • Clustering coefficient
  • Network generation
  • Social networks

ASJC Scopus subject areas

  • Social Sciences(all)
  • Computer Science (miscellaneous)

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