Entropy 2017, 19(7), 321; https://doi.org/10.3390/e19070321
Entropy Characterization of Random Network Models
Pedro J. Zufiria 1,2,*
and Iker Barriales-Valbuena 1
and Iker Barriales-Valbuena 11
Departamento Matemática Aplicada a las TIC, ETSI Telecomunicación, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
2
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)
Abstract
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-TextKeywords:
complex networks; stochastic modelling; entropy; estimation
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Zufiria, P.J.; Barriales-Valbuena, I. Entropy Characterization of Random Network Models. Entropy 2017, 19, 321.
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