Generating random networks with given degree-degree correlations and degree-dependent clustering

TL;DR

This paper introduces an algorithm to generate random networks with customizable degree-degree correlations and degree-dependent clustering, aiding the modeling of complex networks across scientific disciplines.

Contribution

The paper presents a novel algorithm that constructs networks with specified degree correlations and clustering, enhancing the ability to model real-world complex networks.

Findings

Algorithm successfully generates networks matching target properties.

Empirical network data used to verify the algorithm.

Provides a method to fix degree-dependent clustering functions.

Abstract

Random networks are widely used to model complex networks and research their properties. In order to get a good approximation of complex networks encountered in various disciplines of science, the ability to tune various statistical properties of random networks is very important. In this manuscript we present an algorithm which is able to construct arbitrarily degree-degree correlated networks with adjustable degree-dependent clustering. We verify the algorithm by using empirical networks as input and describe additionally a simple way to fix a degree-dependent clustering function if degree-degree correlations are given.