Weighted networks as randomly reinforced urn processes (Articolo in rivista)

Type
Label
  • Weighted networks as randomly reinforced urn processes (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1103/PhysRevE.87.020106 (literal)
Alternative label
  • Guido Caldarelli (1,2,3,4); Alessandro Chessa (2,4); Irene Crimaldi (1); Fabio Pammolli (1,5) (2013)
    Weighted networks as randomly reinforced urn processes
    in Physical review. E, Statistical, nonlinear, and soft matter physics (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Guido Caldarelli (1,2,3,4); Alessandro Chessa (2,4); Irene Crimaldi (1); Fabio Pammolli (1,5) (literal)
Pagina inizio
  • 020106 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.aps.org/doi/10.1103/PhysRevE.87.020106 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 87 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 2 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • (1) IMT Institute for Advanced Studies, Piazza San Ponziano 6, 55100 Lucca, Italy (2) Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, Dip. Fisica Università \"Sapienza,\" P.le A. Moro 2, 00185 Rome, Italy (3) London Institute of Mathematical Sciences, 35a South St., Mayfair London W1K 2XF, United Kingdom (4) Linkalab, Complex Systems Computational Laboratory, 09129 Cagliari, Italy (5) Center for Polymer Studies, Boston University, Boston, Massachusetts 02215, USA (literal)
Titolo
  • Weighted networks as randomly reinforced urn processes (literal)
Abstract
  • We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights are determined by a reinforcement mechanism. We develop a statistical test and a procedure based on it to study the evolution of networks over time, detecting the \"dominance\" of some edges with respect to the others and then assessing if a given instance of the network is taken at its steady state or not. Distance from the steady state can be considered as a measure of the relevance of the observed properties of the network. Our results are quite general, in the sense that they are not based on a particular probability distribution or functional form of the random weights. Moreover, the proposed tool can be applied also to dense networks, which have received little attention by the network community so far, since they are often problematic. We apply our procedure in the context of the International Trade Network, determining a core of \"dominant edges.\" (literal)
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