Du Yuxian, Gao Cai, Chen Xin, Hu Yong, Sadiq Rehan, Deng Yong
School of Computer and Information Science, Southwest University, Chongqing 400715, China.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, 92 Weijin Road, Tianjin 300072, China.
Chaos. 2015 Mar;25(3):033112. doi: 10.1063/1.4916215.
Closeness centrality (CC) measure, as a well-known global measure, is widely applied in many complex networks. However, the classical CC presents many problems for flow networks since these networks are directed and weighted. To address these issues, we propose an effective distance based closeness centrality (EDCC), which uses effective distance to replace conventional geographic distance and binary distance obtained by Dijkstra's shortest path algorithm. The proposed EDCC considers not only the global structure of the network but also the local information of nodes. And it can be well applied in directed or undirected, weighted or unweighted networks. Susceptible-Infected model is utilized to evaluate the performance by using the spreading rate and the number of infected nodes. Numerical examples simulated on four real networks are given to show the effectiveness of the proposed EDCC.
接近中心性（CC）度量作为一种著名的全局度量，在许多复杂网络中得到了广泛应用。然而，经典的CC对于流网络存在许多问题，因为这些网络是有向加权的。为了解决这些问题，我们提出了一种基于有效距离的接近中心性（EDCC），它使用有效距离来代替传统的地理距离以及通过迪杰斯特拉最短路径算法获得的二元距离。所提出的EDCC不仅考虑了网络的全局结构，还考虑了节点的局部信息。并且它可以很好地应用于有向或无向、加权或无权网络。利用易感-感染模型通过传播率和感染节点数来评估性能。给出了在四个真实网络上模拟的数值例子，以表明所提出的EDCC的有效性。
