Strength Distribution in Derivative Networks

Abstract

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 an upper limit and $\gamma >1$ if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

5 pages, 2 figure

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Cited by in corpus (1)

• Predicting the connectivity of primate cortical networks from topological and spatial node properties