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Resampling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network

  • Kazumi Saito
  • Kouzou OharaEmail author
  • Masahiro Kimura
  • Hiroshi Motoda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

We address a problem of efficiently estimating value of a centrality measure for a node in a large network, and propose a sampling-based framework in which only a small number of nodes that are randomly selected are used to estimate the measure. The error estimator we derived is an unbiased estimator of the approximation error defined as the expectation of the difference between the true and the estimated values of the centrality. We experimentally evaluate the fundamental performance of the proposed framework using the closeness and betweenness centralities on six real world networks from different domains, and show that it allows us to estimate the approximation error more tightly and more precisely than the standard error estimator traditionally used based on i.i.d. sampling, i.e., with the confidence level of \(95\%\) for a small number of sampling, say \(20\%\) of the total number of nodes.

Keywords

Error estimation Resampling Node centrality Complex network 

Notes

Acknowledgments

This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 17K00314).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kazumi Saito
    • 1
    • 2
  • Kouzou Ohara
    • 3
    Email author
  • Masahiro Kimura
    • 4
  • Hiroshi Motoda
    • 5
  1. 1.Faculty of ScienceKanagawa UniversityHiratsukaJapan
  2. 2.Center for Advanced Intelligence ProjectRIKENTokyoJapan
  3. 3.College of Science and EngineeringAoyama Gakuin UniversitySagamiharaJapan
  4. 4.Department of Electronics and InformaticsRyukoku UniversityKyotoJapan
  5. 5.Institute of Scientific and Industrial ResearchOsaka UniversitySuitaJapan

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