Next Article in Journal
Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy
Previous Article in Journal
Evaporation Boundary Conditions for the Linear R13 Equations Based on the Onsager Theory
Previous Article in Special Issue
The Relationship between the US Economy’s Information Processing and Absorption Ratios: Systematic vs Systemic Risk
Open AccessArticle

Analysis of Basic Features in Dynamic Network Models

1
Depto. Matemática Aplicada a las TIC, ETSI Telecomunicación, Universidad Politécnica de Madrid, Avda. Complutense 30, 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 2018, 20(9), 681; https://doi.org/10.3390/e20090681
Received: 30 June 2018 / Revised: 1 September 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks. First, some simple dynamic network models, based only on edge density, are analyzed to serve as a baseline reference for assessing more complex models. Then, a model that depends on network structure with the aim of reflecting some characteristics of real networks is also analyzed. Such model shows a more sophisticated behavior with two different regimes, one of them leading to the generation of high clustering coefficient/link density ratio values when compared with the baseline values, as it happens in many real networks. Simulation examples are discussed to illustrate the behavior of the proposed models. View Full-Text
Keywords: complex networks; stochastic modelling; entropy; estimation complex networks; stochastic modelling; entropy; estimation
Show Figures

Figure 1

MDPI and ACS Style

Zufiria, P.J.; Barriales-Valbuena, I. Analysis of Basic Features in Dynamic Network Models. Entropy 2018, 20, 681.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop