Statistical selection of relevant features to classify random, scale free and exponential networks

Claudia Gómez Santillán, Tania Turrubiates López, Laura Cruz Reyes, Eustorgio Meza Conde, Rogelio Ortega Izaguirre

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationElectr., Rob. Autom. Mech. Conf., CERMA - Proc.
Pages376-381
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
EventElectronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Cuernavaca, Morelos, Mexico
Duration: 25 Sep 200728 Sep 2007

Publication series

NameElectronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Proceedings

Conference

ConferenceElectronics, Robotics and Automotive Mechanics Conference, CERMA 2007
Country/TerritoryMexico
CityCuernavaca, Morelos
Period25/09/0728/09/07

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