Subgraph centrality in complex networks.

@article{citeulike:291875, 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.}, address = {Complex Systems Research Group, X-Rays Unit, RIAIDT, Edificio CACTUS, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain. estrada66@yahoo.com}, author = {Estrada, E. and Rodr\'{i}guez-Vel\'{a}zquez, J. A. }, citeulike-article-id = {291875}, issn = {1539-3755}, journal = {Phys Rev E Stat Nonlin Soft Matter Phys}, keywords = {motif}, month = {May}, number = {5 Pt 2}, posted-at = {2008-01-30 15:31:59}, priority = {2}, title = {Subgraph centrality in complex networks.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16089598}, volume = {71}, year = {2005} }

TY - JOUR ID - citeulike:291875 L3 - citeulike-article-id:291875 TI - Subgraph centrality in complex networks. JF - Phys Rev E Stat Nonlin Soft Matter Phys VL - 71 IS - 5 Pt 2 AD - Complex Systems Research Group, X-Rays Unit, RIAIDT, Edificio CACTUS, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain. estrada66@yahoo.com SN - 1539-3755 N2 - 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. KW - motif AU - Estrada, E AU - Rodríguez-Velázquez, JA PY - 2005/05// UR - http://view.ncbi.nlm.nih.gov/pubmed/16089598 ER -