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Identifying high betweenness centrality nodes in large social networks

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Abstract

This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path centrality have high node betweenness centrality. The randomized algorithm runs in time O3 n 2−2αlog n) and outputs, for each vertex v, an estimate of its κ-path centrality up to additive error of ±n 1/2+α with probability 1 − 1/n 2. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.

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Notes

  1. The Hoeffding bound (Hoeffding 1963), a classical result in probability theory, states the following: Let \(X_{1}, X_{2}, \ldots, X_{T}\) be independent random variables, such that each X i ranges over the real interval [a i b i ], and let \(\mu = E[\sum\nolimits_{i=1}^{T} X_{i}/{T}]\) denote the expected value of the average of these variables. Then, for every ξ > 0, \({\rm \hbox{Pr}}[|\frac{\sum_{i=1}^{T} X_{i}}{T} - \mu| \geq \xi] \leq 2e^{-2T^{2}\xi^{2}/\sum_{i=1}^{T} (b_{i} - a_{i})^{2}}. \)

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Acknowledgments

This research was partially supported by the National Science Foundation under Grants No. CNS-0831785 and CNS-0952420. The authors would also like to acknowledge the use of the computing services provided by Research Computing, University of South Florida.

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Correspondence to Nicolas Kourtellis.

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Kourtellis, N., Alahakoon, T., Simha, R. et al. Identifying high betweenness centrality nodes in large social networks. Soc. Netw. Anal. Min. 3, 899–914 (2013). https://doi.org/10.1007/s13278-012-0076-6

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  • DOI: https://doi.org/10.1007/s13278-012-0076-6

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