Discovering global network communities based on local centralities

Bo Yang*, Jiming LIU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

33 Citations (Scopus)


One of the central problems in studying and understanding complex networks, such as online social networks or World Wide Web, is to discover hidden, either physically (e.g., interactions or hyperlinks) or logically (e.g., profiles or semantics) well-defined topological structures. From a practical point of view, a good example of such structures would be so-called network communities. Earlier studies have introduced various formulations as well as methods for the problem of identifying or extracting communities. While each of them has pros and cons as far as the effectiveness and efficiency are concerned, almost none of them has explicitly dealt with the potential relationship between the global topological property of a network and the local property of individual nodes. In order to study this problem, this paper presents a new algorithm, called ICS, which aims to discover natural network communities by inferring from the local information of nodes inherently hidden in networks based on a new centrality, that is, clustering centrality, which is a generalization of eigenvector centrality. As compared with existing methods, our method runs efficiently with a good clustering performance. Additionally, it is insensitive to its built-in parameters and prior knowledge.

Original languageEnglish
Article number9
JournalACM Transactions on the Web
Issue number1
Publication statusPublished - 1 Feb 2008

Scopus Subject Areas

  • Computer Networks and Communications

User-Defined Keywords

  • Centrality
  • Community mining
  • Complex network
  • Graph theory
  • World Wide Web


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