Modelling Multi-Trait Scale-free Networks by Optimization

TL;DR

This paper introduces a universal optimization-based framework for modeling multi-trait scale-free networks, demonstrating its effectiveness in explaining both the scale-free property and other network traits.

Contribution

It presents a novel optimization framework capable of modeling complex networks with multiple traits, unifying explanations for scale-free properties and other network characteristics.

Findings

Optimization explains the origin of scale-free property.

Framework models multiple traits simultaneously.

Effective in generating ideal networks for research.

Abstract

Recently, one paper in Nature(Papadopoulos, 2012) raised an old debate on the origin of the scale-free property of complex networks, which focuses on whether the scale-free property origins from the optimization or not. Because the real-world complex networks often have multiple traits, any explanation on the scale-free property of complex networks should be capable of explaining the other traits as well. This paper proposed a framework which can model multi-trait scale-free networks based on optimization, and used three examples to demonstrate its effectiveness. The results suggested that the optimization is a more generalized explanation because it can not only explain the origin of the scale-free property, but also the origin of the other traits in a uniform way. This paper provides a universal method to get ideal networks for the researches such as epidemic spreading and synchronization on complex networks.