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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 473248, 8 pages
Research Article

On the Distributions of Subgraph Centralities in Complex Networks

1School of Computer Science, Shaanxi Normal University, Xi'an, Shanxi 710062, China
2College of Computer, Qinghai Normal University, Xining, Qinghai 810008, China
3Department of Mathematics, Qinghai Normal University, Xining, Qinghai 810008, China

Received 15 August 2013; Accepted 12 September 2013

Academic Editor: Jinde Cao

Copyright © 2013 Faxu Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Subgraph centrality measure characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than large ones, which makes this measure appropriate for characterizing network motifs. This measure is better in being able to discriminate the nodes of a network than alternate measures. In this paper, the important issue of subgraph centrality distributions is investigated through theory-guided extensive numerical simulations, for three typical complex network models, namely, the ER random-graph networks, WS small-world networks, and BA scale-free networks. It is found that these three very different types of complex networks share some common features, particularly that the subgraph centrality distributions in increasing order are all insensitive to the network connectivity characteristics, and also found that the probability distributions of subgraph centrality of the ER and of the WS models both follow the gamma distribution, and the BA scale-free networks exhibit a power-law distribution with an exponential cutoff.