Strength distribution in gradient networks

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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.