Evolving Clustered Random Networks

TL;DR

This paper introduces a Markov chain simulation method for generating simple, connected random graphs with specific degree sequences and clustering levels, useful for studying network dynamics and structural properties.

Contribution

The paper presents a novel Markov chain algorithm to generate random clustered networks with controlled degree and clustering, serving as versatile models and null models.

Findings

Efficient generation of networks with specified degree and clustering.

Networks are random in all other structural aspects.

Applicable for studying effects of clustering on network processes.

Abstract

We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks.