Structural transitions in scale-free networks
Statistical Mechanics
2009-11-07 v1 Disordered Systems and Neural Networks
Abstract
Real growing networks like the WWW or personal connection based networks are characterized by a high degree of clustering, in addition to the small-world property and the absence of a characteristic scale. Appropriate modifications of the (Barabasi-Albert) preferential attachment network growth capture all these aspects. We present a scaling theory to describe the behavior of the generalized models and the mean field rate equation for the problem. This is solved for a specific case with the result C(k) ~ 1/k for the clustering of a node of degree k. Numerical results agree with such a mean-field exponent which also reproduces the clustering of many real networks.
Cite
@article{arxiv.cond-mat/0208551,
title = {Structural transitions in scale-free networks},
author = {Gabor Szabo and Mikko Alava and Janos Kertesz},
journal= {arXiv preprint arXiv:cond-mat/0208551},
year = {2009}
}
Comments
4 pages, 3 figures, RevTex format