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Riemannian-geometric entropy for measuring network complexity

  Articoli su Riviste JCR/ISI  (anno 2016)

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

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

Riassunto:  A central issue in the science of complex systems is the quantitative characterization of complexity. In the present work we address this issue by resorting to information geometry. Actually we propose a constructive way to associate with a-in principle, any-network a differentiable object (a Riemannian manifold) whose volume is used to define the entropy. The effectiveness of the latter in measuring network complexity is successfully proved through its capability of detecting a classical phase transition occurring in both random graphs and scale-free networks, as well as of characterizing small exponential random graphs, configuration models, and real networks.

Rivista/Giornale:  PHYSICAL REVIEW E
Volume n.:  93 (6)      Pagine da: 062317  a: 062317
Ulteriori informazioni:  We are indebted to R. Quax for providing us with data on power-law random graphs. We also thank M. Rasetti for useful discussions. This work was supported by the Seventh Framework Programme for Research of the European Commission under FET-Proactive Grant TOPDRIM (FP7-ICT-318121).
DOI: 10.1103/PhysRevE.93.062317

*Impact Factor della Rivista: (2016) 2.366   *Citazioni: 6
data tratti da "WEB OF SCIENCE" (marchio registrato di Thomson Reuters) ed aggiornati a:  19/05/2019

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