Centre for Complex Systems Research, Faculty of Engineering and IT, The University of Sydney, New South Wales, Australia.
PLoS One. 2013;8(1):e53095. doi: 10.1371/journal.pone.0053095. Epub 2013 Jan 22.
A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.
有许多中心度指标可用于确定复杂网络中节点的相对重要性，其中介数是突出的。然而，现有的中心度指标在网络渗流场景中并不充分（例如在个体社交网络中的感染传播、计算机网络上的计算机病毒传播或城镇网络上的疾病传播），因为它们没有考虑到单个节点的渗流状态的变化。我们提出了一种新的度量标准——渗流中心度，该度量标准根据节点的拓扑连接性及其渗流状态来量化节点的相对影响。该度量标准可以扩展为包括基于随机游走的定义，并且其计算复杂度与介数中心度相同。我们通过将渗流中心度应用于规范网络以及模拟和真实世界的无标度和随机网络来演示其用法。
