Mechanisms of complex network growth: Synthesis of the preferential attachment and fitness models

• Published in: Physical review. E 2018-06, Vol.97 (6-1), p.062310-062310, Article 062310
• Main Author: Golosovsky, Michael
• Format: Article
• Language: English
• ISSN: 2470-0045; 2470-0053
• DOI: 10.1103/PhysRevE.97.062310

We analyze growth mechanisms of complex networks and focus on their validation by measurements. To this end we consider the equation ΔK=A(t)(K+K_{0})Δt, where K is the node's degree, ΔK is its increment, A(t) is the aging constant, and K_{0} is the initial attractivity. This equation has been commonly used to validate the preferential attachment mechanism. We show that this equation is undiscriminating and holds for the fitness model [Caldarelli et al., Phys. Rev. Lett. 89, 258702 (2002)PRLTAO0031-900710.1103/PhysRevLett.89.258702] as well. In other words, accepted method of the validation of the microscopic mechanism of network growth does not discriminate between "rich-gets-richer" and "good-gets-richer" scenarios. This means that the growth mechanism of many natural complex networks can be based on the fitness model rather than on the preferential attachment, as it was believed so far. The fitness model yields the long-sought explanation for the initial attractivity K_{0}, an elusive parameter which was left unexplained within the framework of the preferential attachment model. We show that the initial attractivity is determined by the width of the fitness distribution. We also present the network growth model based on recursive search with memory and show that this model contains both the preferential attachment and the fitness models as extreme cases.