Evolving scale-free network model with tunable clustering and APL

Abstract

A great number of real network models exhibit the classical characteristics of networks: an exponential degree distribution, high clustering coefficient and a short average path length (APL). In order to construct more really network models, the process of adding edges is divided into two processes based on Wang Bing model that Wang Bing proposes a kind of Evolving scale-free network model with tunable clustering. One process increases the clustering coefficient. Another process reduces the APL. This article proposes a scale-free network model with tunable clustering and APL. Using continuum theory and rate equations method to calculate the degree distribution, the clustering coefficient and APL, the analytical result indicates that the degree distribution follows power law and the clustering coefficient and the APL can be tuned with tow parameters. The APL is estimated analytically, which increases at logarithmically, constantly or negative logarithmically with the time by tuning with tow parameters.