We introduce a new centrality measure that characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than larger ones, which makes this measure appropriate for characterizing network motifs. We show that the subgraph centrality \$C\_S(i)\$ can be obtained mathematically from the spectra of the adjacency matrix of the network. This measure is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness, and eigenvector centralities. We study eight real-world networks for which \$C\_S(i)\$ displays useful and desirable properties, such as clear ranking of nodes and scale-free characteristics. Compared with the number of links per node, the ranking introduced by \$C\_S(i)\$ (for the nodes in the protein interaction network of S. cereviciae) is more highly correlated with the lethality of individual proteins removed from the proteome.
%0 Journal Article
%1 Estrada2005Subgraph
%A Estrada, Ernesto
%A Rodr'ıguez Velázquez, Juan A.
%D 2005
%I American Physical Society
%J Phys. Rev. E
%K centrality networks spectral subgraph
%N 5
%P 056103+
%R 10.1103/physreve.71.056103
%T Subgraph centrality in complex networks
%U http://dx.doi.org/10.1103/physreve.71.056103
%V 71
%X We introduce a new centrality measure that characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than larger ones, which makes this measure appropriate for characterizing network motifs. We show that the subgraph centrality \$C\_S(i)\$ can be obtained mathematically from the spectra of the adjacency matrix of the network. This measure is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness, and eigenvector centralities. We study eight real-world networks for which \$C\_S(i)\$ displays useful and desirable properties, such as clear ranking of nodes and scale-free characteristics. Compared with the number of links per node, the ranking introduced by \$C\_S(i)\$ (for the nodes in the protein interaction network of S. cereviciae) is more highly correlated with the lethality of individual proteins removed from the proteome.
@article{Estrada2005Subgraph,
abstract = {We introduce a new centrality measure that characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than larger ones, which makes this measure appropriate for characterizing network motifs. We show that the subgraph centrality [\${C}\_{S}(i)\$] can be obtained mathematically from the spectra of the adjacency matrix of the network. This measure is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness, and eigenvector centralities. We study eight real-world networks for which \${C}\_{S}(i)\$ displays useful and desirable properties, such as clear ranking of nodes and scale-free characteristics. Compared with the number of links per node, the ranking introduced by \${C}\_{S}(i)\$ (for the nodes in the protein interaction network of S. cereviciae) is more highly correlated with the lethality of individual proteins removed from the proteome.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Estrada, Ernesto and Rodr'{\i}guez Vel\'{a}zquez, Juan A.},
biburl = {https://www.bibsonomy.org/bibtex/24432bc8ca2530ea62b86aa37295aa415/karthikraman},
citeulike-article-id = {4504603},
citeulike-linkout-0 = {http://dx.doi.org/10.1103/physreve.71.056103},
citeulike-linkout-1 = {http://link.aps.org/abstract/PRE/v71/i5/e056103},
citeulike-linkout-2 = {http://link.aps.org/pdf/PRE/v71/i5/e056103},
doi = {10.1103/physreve.71.056103},
interhash = {be0a1cb413f58d8826dd3fd249f680a4},
intrahash = {4432bc8ca2530ea62b86aa37295aa415},
journal = {Phys. Rev. E},
keywords = {centrality networks spectral subgraph},
month = may,
number = 5,
pages = {056103+},
posted-at = {2016-07-11 13:07:25},
priority = {2},
publisher = {American Physical Society},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Subgraph centrality in complex networks},
url = {http://dx.doi.org/10.1103/physreve.71.056103},
volume = 71,
year = 2005
}