Entropy Characterization of Random Network Models
by
Pedro J. Zufiria 1,2,*
and Iker Barriales-Valbuena 1
Pedro J. Zufiria 1,2,* and Iker Barriales-Valbuena 1
1
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.
Entropy 2017, 19(7), 321; https://doi.org/10.3390/e19070321
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)
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
▼
Show Figures
Figure 1
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
MDPI and ACS Style
Zufiria, P.J.; Barriales-Valbuena, I. Entropy Characterization of Random Network Models. Entropy 2017, 19, 321.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
