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Core Decomposition in Directed Networks: Kernelization and Strong Connectivity

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Complex Networks V

Part of the book series: Studies in Computational Intelligence ((SCI,volume 549))

Abstract

In this paper, we propose a method allowing decomposition of directed networks into cores, which final objective is the detection of communities.We based our approach on the fact that a community should be composed of elements having communication in both directions. Therefore, we propose a method based on digraph kernelization and strongly p-connected components. By identifying cores, one can use based-centers clustering methods to generate full communities. Some experiments have been made on three real-world networks, and have been evaluated using the V-Measure, having a more precise analysis through its two sub-measures: homogeneity and completeness. Our work proposes different directions about the use of kernelization into structure analysis, and strong connectivity concept as an alternative to modularity optimization.

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Author information

Authors and Affiliations

  1. CESI, 959 rue de la Bergeresse, 45160, OLIVET, France

    Vincent Levorato

  2. LIFO, Bat. IIIA rue Léonard de Vinci, Université d’Orléans, 45067, ORLEANS, France

    Vincent Levorato

Authors
  1. Vincent Levorato
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Correspondence to Vincent Levorato .

Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bologna, Bologna, Italy

    Pierluigi Contucci

  2. Florida Institute of Technology Dept. Computer Sciences, Melbourne, FL, Florida, USA

    Ronaldo Menezes

  3. Department of Computer Science, University of Bologna, Bologna, Italy

    Andrea Omicini

  4. Room E-136, Northwestern University, Evanston, Illinois, USA

    Julia Poncela-Casasnovas

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Levorato, V. (2014). Core Decomposition in Directed Networks: Kernelization and Strong Connectivity. In: Contucci, P., Menezes, R., Omicini, A., Poncela-Casasnovas, J. (eds) Complex Networks V. Studies in Computational Intelligence, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-05401-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-05401-8_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05400-1

  • Online ISBN: 978-3-319-05401-8

  • eBook Packages: EngineeringEngineering (R0)

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