Maximum entropy principle for Kaniadakis statistics and networks

In this Letter we investigate a connection between Kaniadakis power-law statistics and networks. By following the maximum entropy principle, we maximize the Kaniadakis entropy and derive the optimal degree distribution of complex networks. We show that the degree distribution follows P(k) = P-0 exp(kappa) (-k/eta(kappa)) with exp(kappa)(x) = (root 1 + kappa(2)x(2) + kappa x)(1/kappa), and vertical bar kappa vertical bar < 1. In order to check our approach we study a preferential attachment growth model introduced by Soares et al. [Europhys. Lett. 70 (2005) 70] and a growing random network (GRN) model investigated by Krapivsky et al. [Phys. Rev. Lett. 85 (2000) 4629]. Our results are compared with the ones calculated through the Tsallis statistics. (c) 2013 Elsevier B.V. All rights reserved.