A comprehensive weighted evolving network model

TL;DR

This paper introduces a new weighted evolving network model that captures complex properties like power-law distributions and heavy-tail effects in node degrees, connection weights, and node strengths, improving understanding of real-world weighted networks.

Contribution

The paper presents a novel weighted network model that reproduces key distributional properties observed in real-world systems, extending beyond unweighted network models.

Findings

The model produces power-law distributions for node degrees, connection weights, and node strengths.

It reflects droop-head and heavy-tail effects in the distributions.

Numerical simulations validate the model's ability to mimic real weighted network properties.

Abstract

Many social, technological, biological, and economical systems are best described by weighted networks, whose properties and dynamics depend not only on their structures but also on the connection weights among their nodes. However, most existing research work on complex network models are concentrated on network structures, with connection weights among their nodes being either 1 or 0. In this paper, we propose a new weighted evolving network model. Numerical simulations indicate that this network model yields three power-law distributions of the node degrees, connection weights and node strengths. Particularly, some other properties of the distributions, such as the droop-head and heavy-tail effects, can also be reflected by this model.