Generalized walks-based centrality measures for complex biological networks
Generalized walks-based centrality measures for complex biological networks
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DOI：
10.1016/j.jtbi.2010.01.014
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发表时间：
2010-04-21
影响因子：
2
通讯作者：
Estrada, Ernesto
中科院分区：
文献类型：
--
作者：
Estrada, Ernesto
提出了一种在复杂网络中放大和缩小节点拓扑环境的策略。本文将这种方法推广到复杂网络中节点的子图中心性。在这种情况下，放大策略是基于使用一些已知的矩阵函数，其允许局部地关注节点的环境。当应用缩小策略时，引入新的矩阵函数，其给出节点的拓扑包围的更全局的图片。这些指数允许调制的规模在一个节点的环境影响其中心性。我们应用它们来研究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的异同，以及他们之间的一些经典的中心性措施。我们在这里表明，使用基于放大策略的中心性指数确定了更多的酵母PPI网络中的重要蛋白质比任何其他中心性措施研究。(C)2010爱思唯尔有限公司版权所有。
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. (C) 2010 Elsevier Ltd. All rights reserved.