Volume 107, Number 1, July 2014
|Number of page(s)||6|
|Published online||08 July 2014|
Identifying influential nodes based on local dimension
1 School of Computer and Information Science, Southwest University - Chongqing 400715, China
2 School of Computer Science, Beihang University - Beijing 100191, China
3 School of Science, Hubei University for Nationalities - Enshi 445000, China
4 Center for Quantitative Sciences, Vanderbilt University School of Medicine - Nashville, TN, 37232, USA
5 Department of Biomedical Informatics, Vanderbilt University School of Medicine - Nashville, TN, 37232, USA
6 School of Engineering, Vanderbilt University - TN 37235, USA
Received: 20 March 2014
Accepted: 20 June 2014
How to identify influential nodes in complex networks is still an open issue. In this paper, we propose a novel method to identify influential nodes based on the local dimension (LD) of each node, where low LD values are suggestive of high influence. Applied to four real networks, our method has been demonstrated to have a comparable ability of identifying influential nodes with other commonly used methods. Furthermore, our method performs much better than the k-shell decomposition method, especially in the network with community structure. It can not only identify the influential nodes but also subdivide the nodes in the innermost layers.
PACS: 05.30.Pr – Fractional statistics systems (anyons, etc.) / 05.45.Df – Fractals / 47.53.+n – Fractals in fluid dynamics
© EPLA, 2014
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