Approximate entropy of network parameters

West, James; Lacasa Saiz de Arce, Lucas; Severini, Simone y Teschendorff, Andrew (2012). Approximate entropy of network parameters. "Physical Review e", v. 85 ; pp.. ISSN 1539-3755. https://doi.org/10.1103/PhysRevE.85.046111.

Descripción

Título: Approximate entropy of network parameters
Autor/es:
  • West, James
  • Lacasa Saiz de Arce, Lucas
  • Severini, Simone
  • Teschendorff, Andrew
Tipo de Documento: Artículo
Título de Revista/Publicación: Physical Review e
Fecha: 2012
Volumen: 85
Materias:
Escuela: E.T.S.I. Aeronáuticos (UPM) [antigua denominación]
Departamento: Matemática Aplicada y Estadística [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.

Más información

ID de Registro: 16707
Identificador DC: http://oa.upm.es/16707/
Identificador OAI: oai:oa.upm.es:16707
Identificador DOI: 10.1103/PhysRevE.85.046111
Depositado por: Memoria Investigacion
Depositado el: 05 Nov 2014 18:58
Ultima Modificación: 13 Nov 2014 18:10
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