Identifying and characterizing nodes important to community structure using the spectrum of the graph.

<h4>Background</h4>Many complex systems can be represented as networks, and how a network breaks up into subnetworks or communities is of wide interest. However, the development of a method to detect nodes important to communities that is both fast and accurate is a very challenging and...

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Autores principales:	Yang Wang, Zengru Di, Ying Fan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b8a6920a107745939e73cb15683adb6c
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id	oai:doaj.org-article:b8a6920a107745939e73cb15683adb6c
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spelling	oai:doaj.org-article:b8a6920a107745939e73cb15683adb6c2021-11-18T07:34:23ZIdentifying and characterizing nodes important to community structure using the spectrum of the graph.1932-620310.1371/journal.pone.0027418https://doaj.org/article/b8a6920a107745939e73cb15683adb6c2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110644/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Many complex systems can be represented as networks, and how a network breaks up into subnetworks or communities is of wide interest. However, the development of a method to detect nodes important to communities that is both fast and accurate is a very challenging and open problem.<h4>Methodology/principal findings</h4>In this manuscript, we introduce a new approach to characterize the node importance to communities. First, a centrality metric is proposed to measure the importance of network nodes to community structure using the spectrum of the adjacency matrix. We define the node importance to communities as the relative change in the eigenvalues of the network adjacency matrix upon their removal. Second, we also propose an index to distinguish two kinds of important nodes in communities, i.e., "community core" and "bridge".<h4>Conclusions/significance</h4>Our indices are only relied on the spectrum of the graph matrix. They are applied in many artificial networks as well as many real-world networks. This new methodology gives us a basic approach to solve this challenging problem and provides a realistic result.Yang WangZengru DiYing FanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e27418 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yang Wang Zengru Di Ying Fan Identifying and characterizing nodes important to community structure using the spectrum of the graph.
description	<h4>Background</h4>Many complex systems can be represented as networks, and how a network breaks up into subnetworks or communities is of wide interest. However, the development of a method to detect nodes important to communities that is both fast and accurate is a very challenging and open problem.<h4>Methodology/principal findings</h4>In this manuscript, we introduce a new approach to characterize the node importance to communities. First, a centrality metric is proposed to measure the importance of network nodes to community structure using the spectrum of the adjacency matrix. We define the node importance to communities as the relative change in the eigenvalues of the network adjacency matrix upon their removal. Second, we also propose an index to distinguish two kinds of important nodes in communities, i.e., "community core" and "bridge".<h4>Conclusions/significance</h4>Our indices are only relied on the spectrum of the graph matrix. They are applied in many artificial networks as well as many real-world networks. This new methodology gives us a basic approach to solve this challenging problem and provides a realistic result.
format	article
author	Yang Wang Zengru Di Ying Fan
author_facet	Yang Wang Zengru Di Ying Fan
author_sort	Yang Wang
title	Identifying and characterizing nodes important to community structure using the spectrum of the graph.
title_short	Identifying and characterizing nodes important to community structure using the spectrum of the graph.
title_full	Identifying and characterizing nodes important to community structure using the spectrum of the graph.
title_fullStr	Identifying and characterizing nodes important to community structure using the spectrum of the graph.
title_full_unstemmed	Identifying and characterizing nodes important to community structure using the spectrum of the graph.
title_sort	identifying and characterizing nodes important to community structure using the spectrum of the graph.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/b8a6920a107745939e73cb15683adb6c
work_keys_str_mv	AT yangwang identifyingandcharacterizingnodesimportanttocommunitystructureusingthespectrumofthegraph AT zengrudi identifyingandcharacterizingnodesimportanttocommunitystructureusingthespectrumofthegraph AT yingfan identifyingandcharacterizingnodesimportanttocommunitystructureusingthespectrumofthegraph
_version_	1718423311027798016

Identifying and characterizing nodes important to community structure using the spectrum of the graph.

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