The European Physical Journal B

, Volume 38, Issue 2, pp 311–319 | Cite as

Self-contained algorithms to detect communities in networks

  • C. Castellano
  • F. Cecconi
  • V. LoretoEmail author
  • D. Parisi
  • F. Radicchi


The investigation of community structures in networks is an important issue in many domains and disciplines. In this paper we present a new class of local and fast algorithms which incorporate a quantitative definition of community. In this way the algorithms for the identification of the community structure become fully self-contained and one does not need additional non-topological information in order to evaluate the accuracy of the results. The new algorithms are tested on artificial and real-world graphs. In particular we show how the new algorithms apply to a network of scientific collaborations both in the unweighted and in the weighted version. Moreover we discuss the applicability of these algorithms to other non-social networks and we present preliminary results about the detection of community structures in networks of interacting proteins.


Community Structure Interact Protein Fast Algorithm Scientific Collaboration Weighted Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  • C. Castellano
    • 1
  • F. Cecconi
    • 2
  • V. Loreto
    • 1
    Email author
  • D. Parisi
    • 2
  • F. Radicchi
    • 3
  1. 1.Dipartimento di FisicaUniversitá di Roma “La Sapienza” and INFM-SMC, Unitá di Roma 1RomaItaly
  2. 2.Istituto di Scienze e Tecnologie della CognizioneC.N.R.RomaItaly
  3. 3.Dipartimento di FisicaUniversitá di Roma “Tor Vergata”RomaItaly

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