Hybrid evolving clique-networks and their communicability

TL;DR

This paper introduces a hybrid clique network model with an inhomogeneity parameter to better represent real-world hierarchical networks, analyzing its properties through Monte Carlo simulations.

Contribution

The paper presents a novel hybrid clique network model that combines homogeneous and inhomogeneous components, with tunable inhomogeneity, to study complex network properties.

Findings

Degree distribution varies with inhomogeneity parameter

Average shortest path length depends on network composition

Clustering coefficient and spectrum are influenced by network structure

Abstract

Aiming to understand real-world hierarchical networks whose degree distributions are neither power law nor exponential, we construct a hybrid clique network that includes both homogeneous and inhomogeneous parts, and introduce an inhomogeneity parameter to tune the ratio between the homogeneous part and the inhomogeneous one. We perform Monte-Carlo simulations to study various properties of such a network, including the degree distribution, the average shortest-path-length, the clustering coefficient, the clustering spectrum, and the communicability.