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Node Classification for Signed Social Networks Using Diffuse Interface Methods

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11906))

Abstract

Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Tübingen, Tübingen, Germany

    Pedro Mercado

  2. Department of Computer Science, The University of British Columbia, Vancouver, Canada

    Jessica Bosch

  3. Faculty of Mathematics, Technische Universität Chemnitz, Chemnitz, Germany

    Martin Stoll

Authors
  1. Pedro Mercado
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  2. Jessica Bosch
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  3. Martin Stoll
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Corresponding author

Correspondence to Pedro Mercado .

Editor information

Editors and Affiliations

  1. Leuphana University, Lüneburg, Germany

    Ulf Brefeld

  2. IRISA/Inria, Rennes, France

    Elisa Fromont

  3. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  4. Leiden University, Leiden, The Netherlands

    Arno Knobbe

  5. ETH Zurich, Zurich, Switzerland

    Marloes Maathuis

  6. Institut National des Sciences Appliquées, Villeurbanne, France

    Céline Robardet

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Mercado, P., Bosch, J., Stoll, M. (2020). Node Classification for Signed Social Networks Using Diffuse Interface Methods. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_31

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  • DOI: https://doi.org/10.1007/978-3-030-46150-8_31

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  • Online ISBN: 978-3-030-46150-8

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