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On Wiener polarity index of bicyclic networks

Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices ar...

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Detalles Bibliográficos
Autores principales: Ma, Jing, Shi, Yongtang, Wang, Zhen, Yue, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707490/
https://www.ncbi.nlm.nih.gov/pubmed/26750820
http://dx.doi.org/10.1038/srep19066
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author Ma, Jing
Shi, Yongtang
Wang, Zhen
Yue, Jun
author_facet Ma, Jing
Shi, Yongtang
Wang, Zhen
Yue, Jun
author_sort Ma, Jing
collection PubMed
description Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network’s number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth.
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spelling pubmed-47074902016-01-20 On Wiener polarity index of bicyclic networks Ma, Jing Shi, Yongtang Wang, Zhen Yue, Jun Sci Rep Article Complex networks are ubiquitous in biological, physical and social sciences. Network robustness research aims at finding a measure to quantify network robustness. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks. Wiener type indices are known to depend both on the network’s number of nodes and topology. The Wiener polarity index is also related to the cluster coefficient of networks. In this paper, based on some graph transformations, we determine the sharp upper bound of the Wiener polarity index among all bicyclic networks. These bounds help to understand the underlying quantitative graph measures in depth. Nature Publishing Group 2016-01-11 /pmc/articles/PMC4707490/ /pubmed/26750820 http://dx.doi.org/10.1038/srep19066 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ma, Jing
Shi, Yongtang
Wang, Zhen
Yue, Jun
On Wiener polarity index of bicyclic networks
title On Wiener polarity index of bicyclic networks
title_full On Wiener polarity index of bicyclic networks
title_fullStr On Wiener polarity index of bicyclic networks
title_full_unstemmed On Wiener polarity index of bicyclic networks
title_short On Wiener polarity index of bicyclic networks
title_sort on wiener polarity index of bicyclic networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707490/
https://www.ncbi.nlm.nih.gov/pubmed/26750820
http://dx.doi.org/10.1038/srep19066
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