This article describes a gradient complex network model whose weights are proportional to the difference between uniformly distributed ``fitness'' values assigned to the nodes. It is shown analytically and experimentally that the strength (i.e. the weighted node degree) density of such a network model can be well approximated by a power law with γ≈0.35. Possible implications for neuronal networks topology and dynamics are also discussed.
@article{arxiv.cond-mat/0410230,
title = {Strength distribution in gradient networks},
author = {Luciano da Fontoura Costa},
journal= {arXiv preprint arXiv:cond-mat/0410230},
year = {2007}
}