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Modularity-Driven Kernel k-means for Community Detection

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

The k-means algorithm is probably the most well-known and most popular clustering method in existence today. This work evaluates if a new, autonomous, kernel k-means approach for graph node clustering coupled with the modularity criterion can rival, e.g., the well-established Louvain method. We test the algorithm on social network datasets of various sizes and types. The new method estimates the optimal kernel or distance parameters as well as the natural number of clusters in the dataset based on modularity. Results indicate that this simple black-box algorithm manages to perform on par with the Louvain method given the same input.

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Acknowledgements

This work was supported in part by the FNRS and UCL (FSR) through a PhD scholarship. This work was also partially supported by the Immediate and the Brufence projects funded by InnovIris (Brussels Region). We thank these institutions for giving us the opportunity to conduct both fundamental and applied research.

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Authors and Affiliations

  1. LSM – LouRIM & ICTEAM, Université catholique de Louvain, Chaussée de Binche 151, 7000, Mons, Belgium

    Felix Sommer, François Fouss & Marco Saerens

Authors
  1. Felix Sommer
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  2. François Fouss
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  3. Marco Saerens
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Corresponding author

Correspondence to Felix Sommer .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Sommer, F., Fouss, F., Saerens, M. (2017). Modularity-Driven Kernel k-means for Community Detection. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_48

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_48

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