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Catching homologies by geometric entropy

  Articoli su Riviste JCR/ISI  (anno 2018)

Autori:  Felice D., Franzosi R., Mancini S., Pettini M

Affiliazione Autori:  School of Science and Technology, University of Camerino, I-62032 Camerino, Italy; INFN-Sezione di Perugia, Via A. Pascoli, I-06123 Perugia, Italy; QSTAR and INO-CNR, largo Enrico Fermi 2, I-50125 Firenze, Italy; Aix-Marseille University, Marseille, France; CNRS Centre de Physique Théorique UMR7332, 13288 Marseille, France

Riassunto:  A geometric entropy is defined in terms of the Riemannian volume of the parameter space of a statistical manifold associated with a given network. As such it can be a good candidate for measuring networks complexity. Here we investigate its ability to single out topological features of networks proceeding in a bottom-up manner: first we consider small size networks by analytical methods and then large size networks by numerical techniques. Two different classes of networks, the random graphs and the scale-free networks, are investigated computing their Betti numbers and then showing the capability of geometric entropy of detecting homologies. (C) 2017 Elsevier B.V. All rights reserved.

Volume n.:  491      Pagine da: 666  a: 677
Parole chiave: Complex systems - Differential geometry and topology - Entropy
DOI: 10.1016/j.physa.2017.09.007

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