Strength Distribution in Derivative Networks

Abstract

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed ``fitness&#39;&#39; values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e. the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent $γ<1$ if the fitness has an upper limit and $γ>1$ if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.