Random Networks with Tunable Degree Distribution and Clustering

TL;DR

This paper introduces an algorithm to generate random networks with customizable degree distributions and clustering levels, useful for modeling social networks and studying phase transitions in network connectivity.

Contribution

The paper presents a novel algorithm for creating random networks with arbitrary degree distributions and clustering, enabling more realistic network modeling and analysis.

Findings

Clustering affects the phase transition point for giant component formation.

Networks with different degree distributions exhibit varied behaviors with clustering.

The algorithm allows controlled variation of clustering in networks with specified degree distributions.

Abstract

We present an algorithm for generating random networks with arbitrary degree distribution and Clustering (frequency of triadic closure). We use this algorithm to generate networks with exponential, power law, and poisson degree distributions with variable levels of clustering. Such networks may be used as models of social networks and as a testable null hypothesis about network structure. Finally, we explore the effects of clustering on the point of the phase transition where a giant component forms in a random network, and on the size of the giant component. Some analysis of these effects is presented.