Acceleratingly growing scale-free networks with tunable degree exponents

Wu-Jie Yuan, Xiao-Shu Luo, Jian-Fang Zhou and Bing-Hong Wang

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 21, 5311-5316

Abstract: In this paper, we analytically study the probabilistic accelerating network [M.J. Gagen, J.S. Mattick, Phys. Rev. E 72 (2005) 016123] in its accelerating regimes by using mean field theory. In the growing network, the number of links added with each new node is a nonlinearly increasing function aNβ(t) where N(t) is the number of nodes present at time t. It is found that the network appears to have a power-law degree distribution for large degree with tunable degree exponents (ranging from 3.0 to theoretically infinity) and the degree exponent γ depends only on the parameter β as γ=1+21−β. The analytical results are found to be in good agreement with those obtained by extensive numerical simulations.

Keywords: Scale-free networks; Acceleratingly growing networks; Power-law degree distribution (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:21:p:5311-5316

DOI: 10.1016/j.physa.2008.04.033

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