Department of Mathematics and Statistics, Department of Physics, Institute of Complex Systems, University of Strathclyde, Glasgow G1 1XQ, UK.
J Theor Biol. 2010 Apr 21;263(4):556-65. doi: 10.1016/j.jtbi.2010.01.014. Epub 2010 Jan 18.
A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.
提出了一种在复杂网络中缩放节点拓扑环境的策略。 在这里，我们将这种方法应用于推广复杂网络中节点的子图中心性。 在这种情况下，缩放策略基于使用一些已知的矩阵函数，这些函数允许局部聚焦于节点的环境。 当应用缩放策略时，引入了新的矩阵函数，这些函数提供了节点拓扑环境的更全局视图。 这些指标允许调节节点环境影响其中心度的尺度。 我们将它们应用于研究 10 个蛋白质-蛋白质相互作用（PPI）网络。 我们说明了广义子图中心性指标之间以及它们与一些经典中心性度量之间的相似性和差异。 我们在这里表明，基于缩放策略的中心性指标的使用比所研究的任何其他中心性度量都能在酵母 PPI 网络中识别出更多的必需蛋白质。
