Evolving weighted networks with edge weight dynamical growth

Abstract

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.

Citation

Sun, X., Feng, E., Liu, J. and Wang, B. (2012), "Evolving weighted networks with edge weight dynamical growth", Kybernetes, Vol. 41 No. 9, pp. 1244-1251. https://doi.org/10.1108/03684921211275261