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Centrality Indices

  • Dirk Koschützki
  • Katharina Anna Lehmann
  • Leon Peeters
  • Stefan Richter
  • Dagmar Tenfelde-Podehl
  • Oliver Zlotowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3418)

Abstract

Centrality indices are to quantify an intuitive feeling that in most networks some vertices or edges are more central than others. Many vertex centrality indices were introduced for the first time in the 1950s: e.g., the Bavelas index [50, 51], degree centrality [483] or a first feedback centrality, introduced by Seeley [510]. These early centralities raised a rush of research in which manifold applications were found. However, not every centrality index was suitable to every application, so with time, dozens of new centrality indices were published. This chapter will present some of the more influential, ‘classic’ centrality indices. We do not strive for completeness, but hope to give a catalog of basic centrality indices with some of their main applications.

Keywords

Short Path Adjacency Matrix Centrality Measure Structural Index Betweenness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dirk Koschützki
    • 1
  • Katharina Anna Lehmann
    • 2
  • Leon Peeters
    • 3
  • Stefan Richter
    • 4
  • Dagmar Tenfelde-Podehl
    • 5
  • Oliver Zlotowski
    • 6
  1. 1.IPK GaterslebenGaterslebenGermany
  2. 2.Wilhelm-Schickard-Institut für InformatikUniversität TübingenTübingenGermany
  3. 3.Theoretical Computer ScienceSwiss Federal Institute of Technology ZürichZürichSwitzerland
  4. 4.Theoretical Computer Science, RWTH AachenAachenGermany
  5. 5.Department of MathematicsTechnische Universität KaiserslauternKaiserslauternGermany
  6. 6.Algorithms and Data StructuresUniveristät TrierTrierGermany

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