A NEW CENTRALITY METRIC BASED ON CLUSTERING COEFFICIENT

Chong Li (), Shi-Ze Guo (), Zhe-Ming Lu (), Yu-Long Qiao () and Guang-Hua Song ()
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Chong Li: College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, P. R. China
Shi-Ze Guo: North Electronic Systems Engineering Corporation, Beijing, 100083, P. R. China
Zhe-Ming Lu: School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, P. R. China
Yu-Long Qiao: College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, P. R. China
Guang-Hua Song: School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, P. R. China

International Journal of Modern Physics C (IJMPC), 2013, vol. 24, issue 07, 1-13

Abstract: Many centrality metrics have been proposed over the years to compute the centrality of nodes, which has been a key issue in complex network analysis. The most important node can be estimated through a variety of metrics, such as degree, closeness, eigenvector, betweenness, flow betweenness, cumulated nominations and subgraph. Simulated flow is a common method adopted by many centrality metrics, such as flow betweenness centrality, which assumes that the information spreads freely in the entire network. Generally speaking, the farther the information travels, the more times the information passes the geometric center. Thus, it is easy to determine which node is more likely to be the center of the geometry network. However, during information transmission, different nodes do not share the same vitality, and some nodes are more active than others. Therefore, the product of one node's degree and its clustering coefficient can be viewed as a good factor to show how active this node is. In this paper, a new centrality metric called vitality centrality is introduced, which is only based on this product and the simulated flow. Simulation experiments based on six test networks have been carried out to demonstrate the effectiveness of our new metric.

Keywords: Centrality; vitality centrality; complex network; clustering coefficient; network analysis; 89.75.Hc; 89.75.-k; 02.50.-r (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0129183113500435

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