Strength distribution in gradient networks

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 $\gamma \approx 0.35$. Possible implications for neuronal networks topology and dynamics are also discussed.