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A Genetic Algorithm for Community Detection in Attributed Graphs

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

A genetic algorithm for detecting a community structure in attributed graphs is proposed. The method optimizes a fitness function that combines node similarity and structural connectivity. The communities obtained by the method are composed by nodes having both similar attributes and high link density. Experiments on synthetic networks and a comparison with five state-of-the-art methods show that the genetic approach is very competitive and obtains network divisions more accurate than those obtained by the considered methods.

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Author information

Authors and Affiliations

  1. Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), via P. Bucci 7/11C, 87036, Rende, CS, Italy

    Clara Pizzuti & Annalisa Socievole

Authors
  1. Clara Pizzuti
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  2. Annalisa Socievole
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Corresponding author

Correspondence to Clara Pizzuti .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Pizzuti, C., Socievole, A. (2018). A Genetic Algorithm for Community Detection in Attributed Graphs. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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