Local-world evolving networks with tunable clustering

TL;DR

This paper introduces a flexible network model that captures key properties of real-world networks, such as scale-free degree distribution and high clustering, with tunable clustering coefficient through a simple parameter adjustment.

Contribution

It presents an extended local-world evolving network model with a triad formation step, analytically deriving key network properties and enabling tunable clustering.

Findings

Model reproduces scale-free degree distribution

Achieves high clustering coefficient

Maintains small-world properties

Abstract

We propose an extended local-world evolving network model including a triad formation step. In the process of network evolution, random fluctuation in the number of new edges is involved. We derive analytical expressions for degree distribution, clustering coefficient and average path length. Our model can unify the generic properties of real-life networks: scale-free degree distribution, high clustering and small inter-node separation. Moreover, in our model, the clustering coefficient is tunable simply by changing the expected number of triad formation steps after a single local preferential attachment step.