A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion

The structure properties of complex networks are an open issue. As the most important parameter to describe the structural properties of the complex network, the structure entropy has attracted much attention. Recently, the researchers note that hub repulsion plays an role in structural entropy. In...

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Autores principales:	Fu Tan, Bing Wang, Daijun Wei
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	complex networks structure entropy coulomb's law tsallis nonextensive statistical mechanics Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c63bba5d7bba4b2d831ac749f9afb6dc
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spelling	oai:doaj.org-article:c63bba5d7bba4b2d831ac749f9afb6dc2021-11-29T05:54:12ZA new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion10.3934/mbe.20214551551-0018https://doaj.org/article/c63bba5d7bba4b2d831ac749f9afb6dc2021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021455?viewType=HTMLhttps://doaj.org/toc/1551-0018The structure properties of complex networks are an open issue. As the most important parameter to describe the structural properties of the complex network, the structure entropy has attracted much attention. Recently, the researchers note that hub repulsion plays an role in structural entropy. In this paper, the repulsion between nodes in complex networks is simulated when calculating the structure entropy of the complex network. Coulomb's law is used to quantitatively express the repulsive force between two nodes of the complex network, and a new structural entropy based on the Tsallis nonextensive statistical mechanics is proposed. The new structure entropy synthesizes the influence of repulsive force and betweenness. We study several construction networks and some real complex networks, the results show that the proposed structure entropy can describe the structural properties of complex networks more reasonably. In particular, the new structural entropy has better discrimination in describing the complexity of the irregular network. Because in the irregular network, the difference of the new structure entropy is larger than that of degree structure entropy, betweenness structure entropy and Zhang's structure entropy. It shows that the new method has better discrimination for irregular networks, and experiments on Graph, Centrality literature, US Aire lines and Yeast networks confirm this conclusion.Fu TanBing WangDaijun WeiAIMS Pressarticlecomplex networksstructure entropycoulomb's lawtsallis nonextensive statistical mechanicsBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9253-9263 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	complex networks structure entropy coulomb's law tsallis nonextensive statistical mechanics Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	complex networks structure entropy coulomb's law tsallis nonextensive statistical mechanics Biotechnology TP248.13-248.65 Mathematics QA1-939 Fu Tan Bing Wang Daijun Wei A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
description	The structure properties of complex networks are an open issue. As the most important parameter to describe the structural properties of the complex network, the structure entropy has attracted much attention. Recently, the researchers note that hub repulsion plays an role in structural entropy. In this paper, the repulsion between nodes in complex networks is simulated when calculating the structure entropy of the complex network. Coulomb's law is used to quantitatively express the repulsive force between two nodes of the complex network, and a new structural entropy based on the Tsallis nonextensive statistical mechanics is proposed. The new structure entropy synthesizes the influence of repulsive force and betweenness. We study several construction networks and some real complex networks, the results show that the proposed structure entropy can describe the structural properties of complex networks more reasonably. In particular, the new structural entropy has better discrimination in describing the complexity of the irregular network. Because in the irregular network, the difference of the new structure entropy is larger than that of degree structure entropy, betweenness structure entropy and Zhang's structure entropy. It shows that the new method has better discrimination for irregular networks, and experiments on Graph, Centrality literature, US Aire lines and Yeast networks confirm this conclusion.
format	article
author	Fu Tan Bing Wang Daijun Wei
author_facet	Fu Tan Bing Wang Daijun Wei
author_sort	Fu Tan
title	A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
title_short	A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
title_full	A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
title_fullStr	A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
title_full_unstemmed	A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
title_sort	new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/c63bba5d7bba4b2d831ac749f9afb6dc
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A new structural entropy measurement of networks based on the nonextensive statistical mechanics and hub repulsion

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