Mean-field theory for clustering coefficients in Barabasi-Albert networks

TL;DR

This paper develops a mean field theoretical approach to analyze clustering coefficients in Barabasi-Albert networks, revealing degree-dependent local clustering and accurately predicting network-wide clustering.

Contribution

Introduces a novel mean field method to analytically determine clustering coefficients in BA networks, validated by numerical simulations.

Findings

Local clustering depends on node degree

Analytic results match numerical simulations for low-degree nodes

Network-wide clustering coefficients are accurately predicted

Abstract

We applied a mean field approach to study clustering coefficients in Barabasi-Albert networks. We found that the local clustering in BA networks depends on the node degree. Analytic results have been compared to extensive numerical simulations finding a very good agreement for nodes with low degrees. Clustering coefficient of a whole network calculated from our approach perfectly fits numerical data.