SPR-based Markov chain method for degree distributions of evolving networks

Xiaojun Zhang, Zishu He, Zheng He and Lez Rayman-Bacchus

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 11, 3350-3358

Abstract: In this paper, we develop a stochastic process rules (SPR) based Markov chain method to calculate the degree distributions of evolving networks. This new approach overcomes two shortcomings of Shi, Chen and Liu’s use of the Markov chain method (Shi et al. 2005 [21]). In addition we show how an SPR-based Markov chain method can be effectively used to calculate degree distributions of random birth-and-death networks, which we believe to be novel. First SPR are introduced to replace traditional evolving rules (TR), making it possible to compute degree distributions in one sample space. Then the SPR-based Markov chain method is introduced and tested by using it to calculate two kinds of evolving network. Finally and most importantly, the SPR-based method is applied to the problem of calculating the degree distributions of random birth-and-death networks.

Keywords: Degree distributions; SPR-based Markov chain; Evolving networks; Random birth-and-death networks (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:11:p:3350-3358

DOI: 10.1016/j.physa.2012.01.040

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