Strength distribution in derivative networks
This article describes a complex network model whose weights are proportional to the difference between uniformly distributed "fitness" 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 gamma   1 if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics axe also discussed.
strength distribution complex networks fitness weighted diagraphs node similarity