Evolving weighted networks with edge weight dynamical growth

Purpose - The purpose of this paper is to study some evolving mechanisms for producing weighted networks, as well as to analyze the statistical properties of the networks. Design/methodology/approach - A simple one-parameter evolution model of weighted networks is proposed, in which the topological growth combines with the variation of weights. Based on weight-driven dynamics, the model can generate scale-free distributions of the degree, node strength and edge weight, as confirmed in many real networks. Findings - The exponent of the edge weight can be widely tuned. The unique parameter p controls the edge weight dynamical growth. The authors also obtain the non-trivial weighted clustering coefficient and the weighted average to the nearest neighbors' degree. Research limitations/implications - Accessibility and availability of data are the main limitations which apply to the figures. Practical implications - The new evolving networks method may be beneficial for understanding real networks. Originality/value - The paper proposes a new approach of explaining the evolving mechanisms of the real networks.