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Statistical Selection of Relevant Features to Classify Random, Scale Free and Exponential Networks

  • Laura Cruz Reyes
  • Eustorgio Meza Conde
  • Tania Turrubiates López
  • Claudia Guadalupe Gómez Santillán
  • Rogelio Ortega Izaguirre
Part of the Advances in Soft Computing book series (AINSC, volume 44)

Abstract

In this paper a statistical selection of relevant features is presented. An experiment was designed to select relevant and not redundant features or characterization functions, which allow quantitatively discriminating among different types of complex networks. As well there exist researchers given to the task of classifying some networks of the real world through characterization functions inside a type of complex network, they do not give enough evidences of detailed analysis of the functions that allow to determine if all are necessary to carry out an efficient discrimination or which are better functions for discriminating. Our results show that with a reduced number of characterization functions such as the shortest path length, standard deviation of the degree, and local efficiency of the network can discriminate efficiently among the types of complex networks treated here.

Keywords

Complex Networks Internet Modeling Classification Variable Selection Experimental Design 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Laura Cruz Reyes
    • 1
  • Eustorgio Meza Conde
    • 2
  • Tania Turrubiates López
    • 1
    • 3
  • Claudia Guadalupe Gómez Santillán
    • 1
    • 2
  • Rogelio Ortega Izaguirre
    • 2
  1. 1.Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA)Altamira, TamaulipasMéxico
  2. 2.Instituto Tecnológico de Ciudad Madero (ITCM)TamaulipasMéxico
  3. 3.Instituto Tecnológico Superior de Álamo Temapache (ITSAT)Alamo, VeracruzMéxico

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