Open AccessAn accelerated growth model to generate complex networks with connectivity distribution slope that varies with time
Author(s) -
P. Castillo Castillo,
Pedro David Arjona-Villicaña,
J. Acosta-Elías
Publication year - 2019
Publication title -
revista mexicana de física/revista mexicana de física
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.181
H-Index - 25
eISSN - 2683-2224
pISSN - 0035-001X
DOI - 10.31349/revmexfis.65.128
Subject(s) - degree (music) , degree distribution , complex network , computer science , complex system , range (aeronautics) , preferential attachment , power law , statistical physics , network model , topology (electrical circuits) , data mining , physics , mathematics , artificial intelligence , statistics , aerospace engineering , combinatorics , world wide web , acoustics , engineering
Many real-life complex networks have in-degree and out-degree distributions that decay as apower-law. However, the few models that have been able to reproduce both of these properties,cannot reproduce the wide range of values found in real systems. Another limitation of thesemodels is that they add links from nodes which are created into the network, as well as betweennodes already present in this network. However, adding links between existing nodes is not acharacteristic available in all systems. This paper introduces a new complex network growthmodel that, without adding links between existing nodes is able to generate complex topologieswith in-degree and out-degree distributions that decay as a power-law. Moreover, in this growthmodel, the ratio at which links are created is greater than the ratio at which nodes are born, whichproduces an accelerated growth phenomenon that can be found in some real systems, like theInternet at the Autonomous System level.