Self-contained algorithms to detect communities in networks (Articolo in rivista)

Type
Label
  • Self-contained algorithms to detect communities in networks (Articolo in rivista) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1140/epjb/e2004-00123-0 (literal)
Alternative label
  • Claudio Castellano (1), Federico Cecconi (2), Vittorio Loreto (1), Domenico Parisi (2), Filippo Radicchi (3) (2004)
    Self-contained algorithms to detect communities in networks
    in The European physical journal. B, Condensed matter physics (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Claudio Castellano (1), Federico Cecconi (2), Vittorio Loreto (1), Domenico Parisi (2), Filippo Radicchi (3) (literal)
Pagina inizio
  • 311 (literal)
Pagina fine
  • 319 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 38 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 2 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1) Dipartimento di Fisica, Universitá di Roma \"La Sapienza\" and INFM-SMC, Unitá di Roma 1, P.le A. Moro 5, 00185, Roma, Italy (2) Istituto di Scienze e Tecnologie della Cognizione, C.N.R., Viale Marx, 15, 00137, Roma, Italy (3) Dipartimento di Fisica, Universitá di Roma \"Tor Vergata\", Via della Ricerca Scientifica 1, 00133, Roma, Italy (literal)
Titolo
  • Self-contained algorithms to detect communities in networks (literal)
Abstract
  • 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. (literal)
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