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The Analysis of the Power Law Feature in Complex Networks
Consensus about the universality of the power law feature in complex networks is experiencing widespread challenges. In this paper, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that are more common than BA...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689370/ https://www.ncbi.nlm.nih.gov/pubmed/36359650 http://dx.doi.org/10.3390/e24111561 |
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author | Zhang, Xiaojun He, Zheng Zhang, Liwei Rayman-Bacchus, Lez Shen, Shuhui Xiao, Yue |
author_facet | Zhang, Xiaojun He, Zheng Zhang, Liwei Rayman-Bacchus, Lez Shen, Shuhui Xiao, Yue |
author_sort | Zhang, Xiaojun |
collection | PubMed |
description | Consensus about the universality of the power law feature in complex networks is experiencing widespread challenges. In this paper, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that are more common than BA networks in the real world, and then we calculate their degree distributions; the results show that the tails of their degree distributions exhibit a distinct power law feature. Second, we suggest that in the real world two important factors—network size and node disappearance probability—will affect the analysis of power law characteristics in observation networks. Finally, we suggest that an effective way of detecting the power law property is to observe the asymptotic (limiting) behavior of the degree distribution within its effective intervals. |
format | Online Article Text |
id | pubmed-9689370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96893702022-11-25 The Analysis of the Power Law Feature in Complex Networks Zhang, Xiaojun He, Zheng Zhang, Liwei Rayman-Bacchus, Lez Shen, Shuhui Xiao, Yue Entropy (Basel) Article Consensus about the universality of the power law feature in complex networks is experiencing widespread challenges. In this paper, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that are more common than BA networks in the real world, and then we calculate their degree distributions; the results show that the tails of their degree distributions exhibit a distinct power law feature. Second, we suggest that in the real world two important factors—network size and node disappearance probability—will affect the analysis of power law characteristics in observation networks. Finally, we suggest that an effective way of detecting the power law property is to observe the asymptotic (limiting) behavior of the degree distribution within its effective intervals. MDPI 2022-10-29 /pmc/articles/PMC9689370/ /pubmed/36359650 http://dx.doi.org/10.3390/e24111561 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Xiaojun He, Zheng Zhang, Liwei Rayman-Bacchus, Lez Shen, Shuhui Xiao, Yue The Analysis of the Power Law Feature in Complex Networks |
title | The Analysis of the Power Law Feature in Complex Networks |
title_full | The Analysis of the Power Law Feature in Complex Networks |
title_fullStr | The Analysis of the Power Law Feature in Complex Networks |
title_full_unstemmed | The Analysis of the Power Law Feature in Complex Networks |
title_short | The Analysis of the Power Law Feature in Complex Networks |
title_sort | analysis of the power law feature in complex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689370/ https://www.ncbi.nlm.nih.gov/pubmed/36359650 http://dx.doi.org/10.3390/e24111561 |
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