Detection of topological patterns in complex networks: Correlation profile of the internet

Sergei Maslov, Kim Sneppen, Alexei Zaliznyak

Research output: Contribution to journalArticlepeer-review

Abstract

A general scheme for detecting and analyzing topological patterns in large complex networks is presented. In this scheme the network in question is compared with its properly randomized version that preserves some of its low-level topological properties. Statistically significant deviation of any topological property of a network from this null model likely reflects its design principles and/or evolutionary history. We illustrate this basic scheme using the example of the correlation profile of the Internet quantifying correlations between degrees of its neighboring nodes. This profile distinguishes the Internet from previously studied molecular networks with a similar scale-free degree distribution. We finally demonstrate that the clustering in a network is very sensitive to both the degree distribution and its correlation profile and compare the clustering in the Internet to the appropriate null model.

Original languageEnglish (US)
Pages (from-to)529-540
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Volume333
Issue number1-4
DOIs
StatePublished - Feb 15 2004
Externally publishedYes

Keywords

  • Cliquishness
  • Correlation profile
  • Metropolis
  • Network motifs
  • Random networks
  • Scale free networks

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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