q Index Degree Distribution in Random Networks via Superstatistics

Fuente: arXiv
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Autores principales: Wang, Huilin, Deng, Weibing
Formato: Preprint
Publicado: 2024
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author Wang, Huilin
Deng, Weibing
author_facet Wang, Huilin
Deng, Weibing
contents 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 λ, 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.
format Preprint
id arxiv_https___arxiv_org_abs_2411_08066
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle q Index Degree Distribution in Random Networks via Superstatistics
Wang, Huilin
Deng, Weibing
Physics and Society
Data Analysis, Statistics and Probability
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 λ, 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.
title q Index Degree Distribution in Random Networks via Superstatistics
topic Physics and Society
Data Analysis, Statistics and Probability
url https://arxiv.org/abs/2411.08066