Constrained Randomisation of Weighted Networks

TL;DR

This paper introduces a Markov chain method for generating surrogate weighted networks that preserve vertex strengths or edge weights, aiding in the analysis and interpretation of complex networks like brain and trade networks.

Contribution

The paper presents a novel Markov chain approach for creating constrained surrogate networks that maintain specific weight properties, enhancing network analysis.

Findings

Surrogate networks reveal network-specific characteristics.

Method applied to brain and trade networks.

Provides insights into network structure and interpretation.

Abstract

We propose a Markov chain method to efficiently generate 'surrogate networks' that are random under the constraint of given vertex strengths. With these strength-preserving surrogates and with edge-weight-preserving surrogates we investigate the clustering coefficient and the average shortest path length of functional networks of the human brain as well as of the International Trade Networks. We demonstrate that surrogate networks can provide additional information about network-specific characteristics and thus help interpreting empirical weighted networks.