On computation of clustering coefficient in a class of random networks

TL;DR

This paper develops methods to compute clustering coefficients and mean degree of separation in complex random networks with added metric and symmetry structures, providing detailed analysis of representative models.

Contribution

It introduces a novel approach to calculating key network metrics in structured random networks, extending existing models with metric and symmetry considerations.

Findings

Effective computation of clustering coefficient and mean degree of separation

Application to various structured random network models

Enhanced understanding of network properties with added structures

Abstract

The random networks enriched with additional structures as metric and group-symmetry in background metric space are investigated. The important quantities like he clustering coefficient as well as the mean degree of separation in such networks are effectively computed with help of additional structures. Representative models are discussed in details.