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Entropy 2017, 19(7), 321;

Entropy Characterization of Random Network Models

Departamento Matemática Aplicada a las TIC, ETSI Telecomunicación, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, E-28040 Madrid, Spain
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 24 June 2017 / Accepted: 27 June 2017 / Published: 30 June 2017
(This article belongs to the Special Issue Complex Systems, Non-Equilibrium Dynamics and Self-Organisation)
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This paper elaborates on the Random Network Model (RNM) as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc.) and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework. View Full-Text
Keywords: complex networks; stochastic modelling; entropy; estimation complex networks; stochastic modelling; entropy; estimation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zufiria, P.J.; Barriales-Valbuena, I. Entropy Characterization of Random Network Models. Entropy 2017, 19, 321.

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