Defining and identifying communities in networks (Articolo in rivista)

Type
Label
  • Defining and identifying 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
  • 10.1073/pnas.0400054101 (literal)
Alternative label
  • Filippo Radicchi (1); Claudio Castellano (2); Federico Cecconi (3); Vittorio Loreto (2); Domenico Parisi (3) (2004)
    Defining and identifying communities in networks
    in Proceedings of the National Academy of Sciences of the United States of America
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Filippo Radicchi (1); Claudio Castellano (2); Federico Cecconi (3); Vittorio Loreto (2); Domenico Parisi (3) (literal)
Pagina inizio
  • 2658 (literal)
Pagina fine
  • 2663 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.pnas.org/content/101/9/2658.short (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 101 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1) Dipartimento di Fisica, Università di Roma \"Tor Vergata,\" Via della Ricerca Scientifica 1, 00133 Roma, Italy; (2) Dipartimento di Fisica, Università di Roma \"La Sapienza\" and Istituto Nazionale per la Fisica della Materia, Statistical Mechanics and Complexity Center, Unità di Roma 1, Piazzale A. Moro 5, 00185 Roma, Italy; (3) Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche, Viale Marx, 15, 00137 Rome, Italy (literal)
Titolo
  • Defining and identifying communities in networks (literal)
Abstract
  • The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artificial and real-world graphs. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems. (literal)
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