Mean-field theory for scale-free random networks

Albert László Barabási, Réka Albert, Hawoong Jeong

Research output: Contribution to journalArticlepeer-review

1801 Scopus citations

Abstract

Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information is available display scale-free features. Here we study the scaling properties of the recently introduced scale-free model, that can account for the observed power-law distribution of the connectivities. We develop a mean-field method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the scaling exponents. The mean-field method can be used to address the properties of two variants of the scale-free model, that do not display power-law scaling.

Original languageEnglish (US)
Pages (from-to)173-187
Number of pages15
JournalPhysica A: Statistical Mechanics and its Applications
Volume272
Issue number1
DOIs
StatePublished - Oct 1 1999

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

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