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Sponge Walker: Community Detection in Large Directed Social Networks Using Local Structures and Random Walks

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Network Intelligence Meets User Centered Social Media Networks (ENIC 2017)

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

Community detection is one of the most central topics in social network analysis. Yet, many methods that account for directed interactions in social networks scale poorly. Some methods that have lower complexity focus on local criteria, ignoring larger structures. This paper proposes an iterative community detection algorithm that captures local structure in its first iterations and then performs random walks to capture larger structures on a reduced network. The proposed algorithm returns a dendrogram and also suggests the best partition of a network. Experimentations on synthetic social networks were performed and comparisons with benchmark community detection algorithms show that the Sponge walker detects high quality clusters despite low time complexity.

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Acknowledgements

This work was supported by the French Investment for the future project REQUEST (REcursive QUEry and Scalable Technologies) and the region of Champagne-Ardenne.

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Authors and Affiliations

  1. Charles Delaunay Institute, UMR CNRS 6281, University of Technology of Troyes, Troyes, France

    Omar Jaafor & Babiga Birregah

Authors
  1. Omar Jaafor
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  2. Babiga Birregah
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Correspondence to Omar Jaafor .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    H. Ulrich Hoppe

  3. Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    Tobias Hecking

  4. Department of Computational Intelligence The Engine Centre, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Piotr Bródka

  5. Faculty of Computer Science and Management The Engine Centre, Wroclaw University of Science and Technology, Wroclaw, Poland

    Przemyslaw Kazienko

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Jaafor, O., Birregah, B. (2018). Sponge Walker: Community Detection in Large Directed Social Networks Using Local Structures and Random Walks. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-90312-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90311-8

  • Online ISBN: 978-3-319-90312-5

  • eBook Packages: Social SciencesSocial Sciences (R0)

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