q Index Degree Distribution in Random Networks via Superstatistics

TL;DR

This paper introduces a superstatistical framework to generate and analyze complex networks with tunable degree distributions, enhancing the understanding of scale-free network properties and their topological features.

Contribution

It presents a novel superstatistical method for constructing scale-free networks with adjustable degree distribution characteristics, expanding the modeling toolkit.

Findings

Enhanced flexibility in network topology and transport properties.

Ability to smoothly transition between different degree distribution regimes.

Improved resilience and small-world features in constructed networks.

Abstract

In this study, we employ a superstatistical approach to construct q exponential and q Maxwell Boltzmann complex networks, generalizing the concept of scale free networks. By adjusting the crossover parameter {\lambda}, we control the degree of the q exponential plateau at low node degrees, allowing a smooth transition to pure power law degree distributions. Similarly, the parameter b modulates the q Maxwell Boltzmann curvature, facilitating a shift toward pure power law networks. This framework introduces a novel perspective for constructing and analyzing scale free networks. Our results show that these additional degrees of freedom significantly enhance the flexibility of both network types in terms of topological and transport properties, including clustering coefficients, small world characteristics, and resilience to attacks. Future research will focus on exploring the dynamic properties of these networks, offering promising directions for further investigation.