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 tness, 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 is dynamically updated to account for the evolving degree distribution. It is shown that in the case of linear mapping between fitnesses and degrees, the models lead to tunable stationary powerlaw degree distribution, while in the non-linear case the distributions converge to the stretched exponential form.