Social Network Analysis and Mining

, Volume 3, Issue 4, pp 899–914 | Cite as

Identifying high betweenness centrality nodes in large social networks

  • Nicolas Kourtellis
  • Tharaka Alahakoon
  • Ramanuja Simha
  • Adriana Iamnitchi
  • Rahul Tripathi
Original Article

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.

Keywords

Betweenness centrality Social network analysis Algorithms Experimental evaluation 

Notes

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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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Nicolas Kourtellis
    • 1
  • Tharaka Alahakoon
    • 2
  • Ramanuja Simha
    • 3
  • Adriana Iamnitchi
    • 1
  • Rahul Tripathi
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.NorcrossUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of DelawareNewarkUSA

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