Optimal ranking in networks with community structure

Huafeng Xie, Koon Kiu Yan, Sergei Maslov

Research output: Contribution to journalArticlepeer-review


The World-Wide Web (WWW) is characterized by a strong community structure in which groups of webpages (e.g. those devoted to a common topic or belonging to the same organization) are densely interconnected by hyperlinks. We study how such network architecture affects the average Google rank of individual communities. Using a mean-field approximation, we quantify how the average Google rank of community webpages depend on the degree to which it is isolated from the rest of the world in both incoming and outgoing directions, and α the only intrinsic parameter of Google's PageRank algorithm. Based on this expression we introduce a concept of a web-community being decoupled or conversely coupled to the rest of the network. We proceed with empirical study of several internal web-communities within two US universities. The predictions of our mean-field treatment were qualitatively verified in those real-life networks. Furthermore, the value α = 0.15 used by Google seems to be optimized for the degree of isolation of communities as they exist in the actual WWW.

Original languageEnglish (US)
Pages (from-to)831-836
Number of pages6
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - Jan 1 2007
Externally publishedYes


  • Community structure
  • Networks
  • PageRank
  • Ranking
  • WWW

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


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