Fitness-based network growth with dynamic feedback

Abstract

We study a class of network growth models in which the choice of attachment by new nodes is governed by intrinsic attractiveness, or fitness, of the existing nodes. The key feature of the models is a feedback mechanism whereby the distribution from which fitnesses of new nodes are drawn derives from the evolving instantaneous node degree distribution. In the case of linear mapping between fitnesses and degrees, the fixed point degree distribution is asymptotically power-law, while in the nonlinear case the distributions converge to the stretched exponential form.