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The Classification of random graph models using graph centralities

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(7), pp.61-69
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 2, 2019
  • Accepted : June 27, 2019
  • Published : July 31, 2019

Tae-Soo cho 1 Chi-Geun Han 1 Sang-Hoon Lee 1

1경희대학교

Accredited

ABSTRACT

In this paper, a classification method of random graph models is proposed and it is based on centralities of the random graphs. Similarity between two random graphs is measured for the classification of random graph models. The similarity between two random graph models    and    is defined by the distance of    and   , where    is a set of random graph         that have the same number of nodes and edges as random graph   . The distance(   ,  ) is obtained by comparing centralities of    and    . Through the computational experiments, we show that it is possible to compare random graph models regardless of the number of vertices or edges of the random graphs. Also, it is possible to identify and classify the properties of the random graph models by measuring and comparing similarities between random graph models.

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