Clustering coefficient of random intersection graphs with infinite degree variance

TL;DR

This paper studies the clustering coefficient in a specific type of random intersection graph characterized by a power law degree sequence with finite mean but infinite variance, revealing its asymptotic behavior.

Contribution

It introduces the analysis of the global clustering coefficient's distribution in such graphs, highlighting its tunability and asymptotic properties.

Findings

Global clustering coefficient has a tunable asymptotic distribution.

Applicable to graphs with power law degree sequences with infinite variance.

Provides insights into clustering behavior in complex networks.

Abstract

For a random intersection graph with a power law degree sequence having a finite mean and an infinite variance we show that the global clustering coefficient admits a tunable asymptotic distribution.