A Model Interpolating Between Regular And Scale Free Network With Tunable Exponent
[Full Text]
AUTHOR(S)
Md. Kamruzzaman, Afrina Sharmin
KEYWORDS
Index Terms:  nodes, links, hubs, preferential attachment rule, powerlaw degree distribution, fat tail, cumulative distribution.
ABSTRACT
Abstract:  The purpose of this paper is to investigate a simple network model whereby a new node is either attached to the youngest of the existing node with probability p or it is attached with probability (1  p) to any of the existing node following the preferential attachment rule. For 0≤p<1 the model exhibits power law degree distribution P(k)~k^(γ) with γ=3+p⁄((1p).) The model thus nicely interpolates between the regular graph at p = 1 with P(k)~δ(k2) and the BarabásiAlbert model at p = 0 with P(k)~k^(3).
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