Subgraph centrality in complex networks.

By Ernesto Estrada, Juan Alberto Rodríguez-Velázquez. Physical review. E, Statistical, nonlinear, and soft matter physics, 2005

1,202 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ae743cde0347c3be6376734c882fc42bfc9463f9

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Citations in the corpus

Why Do Hubs in the Yeast Protein Interaction Network Tend To Be Essential: Reexamining the Connection between the Network Topology and Essentiality (2008, background): “To clarify the topological role of hubs in the tested networks, we compared degree centrality to two other local indices (eigenvector centrality (EC) [28] and subgraph centrality (SC) [29]) and to two betweenness indices (shortest-path betweenness centrality (SPBC) [30] and current-flow betweenness ”
The Physics of Communicability in Complex Networks (2011, background): “The spectral measure of bipartivity is the ratio of the self-communicability of a node based only on even closed walks to the total self-communicability of this node (Estrada and Rodríguez-Velazquez, 2005b): , 1 ( ) cosh / exp cosh / . n S j A j b G tr A tr A EE G”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Recently a number of centralitymeasures have beendefined based on network dynamics (Freeman, 1978; Estrada & Rodríguez-Velázquez, 2005; Estrada, 2006; Hwang et al., 2008; Kovács et al., 2010; Du et al., 2012; Ghosh & Lerman, 2012; Grady et al., 2012; Grassler et al., 2012; Joseph&Chen,…”
Vital nodes identification in complex networks (2016, background): “However, if the network is a simple and connected network with n > 1, the subgraph centrality of node vi satisfies the inequality [47] SC (i) ≤ 1 n (”
Characterization of complex networks: A survey of measurements (2005, background): “Recently, a way to quantify the centrality of a node based on the number of subgraphs in which the node takes part was proposed [63].”
CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks (2015, background): “Estrada and Rodriguez-Velazquez, 2005 Information Centrality (IC) is the adjacency matrix of network, D is the diagonal matrix of all nodes ’ degree, J is a matrix whose all elements are 1.”
Linear and Multilinear Algebra (2007, background): “Moreover, an application of our bipartivity measures to the study of fullerene graphs can be found in [7].”
Graph theory and networks in Biology. (2006, background): “In [119, 120], it was found that Csg performed better than most other centrality measures, including degree centrality, in predicting essentiality in the yeast protein interaction network.”
Some unique properties of eigenvector centrality (2007, background): “…varying the degree to which status is transmitted from one vertex to another and it also permits the assessment of power in negatively connected (Cook et al., 1983) exchange networks when being connected to exploitable isolates increases ones power: c(β) = ∞ k=1 β k−1 A k 1 (2) where |β| 1/λ…”
The von Neumann Entropy of Networks (2008, background): “Notably, graph-states and spin networks, just to mention two major ones: graph-states are certain quantum error correcting codes, important for characterizing the computational resources in measurement based quantum computation [6, 19]; spin networks are arrangements of interacting quantum…”