Random intersection graph process

TL;DR

This paper introduces a random intersection graph process to model evolving sparse networks with tunable degree distributions, assortativity, and clustering, providing explicit formulas for key network properties.

Contribution

It presents a novel random intersection graph process with analytical formulas for degree distribution, assortativity, and clustering in sparse evolving networks.

Findings

Asymptotic degree distribution derived

Explicit formulas for assortativity and clustering coefficients provided

Model effectively captures properties of real-world affiliation networks

Abstract

We introduce a random intersection graph process aimed at modeling sparse evolving affiliation networks that admit tunable (power law) degree distribution and assortativity and clustering coefficients. We show the asymptotic degree distribution and provide explicit asymptotic formulas for assortativity and clustering coefficients.