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An Efficient Entropy-Based Graph Kernel

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Graph-Based Representations in Pattern Recognition (GbRPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14121))

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Abstract

Graph kernels are methods used in machine learning algorithms for handling graph-structured data. They are widely used for graph classification in various domains and are particularly valued for their accuracy. However, most existing graph kernels are not fast enough. To address this issue, we propose a new graph kernel based on the concept of entropy. Our method has the advantage of handling labeled and attributed graphs while significantly reducing computation time when compared to other graph kernels. We evaluated our method on several datasets and compared it with various state-of-the-art techniques. The results show a clear improvement in the performance of the initial method. Furthermore, our findings rank among the best in terms of classification accuracy and computation speed compared to other graph kernels.

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Acknowledgments

This work was funded by Agence Nationale de la Recherche (ANR) under grant ANR-20-CE39-0008 and Département INFO-BOURG IUT Lyon1, campus de Bourg en Bresse.

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Authors and Affiliations

  1. École nationale Supérieure d’Informatique (ESI), Oued Smar, Alger, Algérie

    Aymen Ourdjini

  2. Université de Lyon, Université Lyon 1, LIRIS UMR 5205, 69622, Lyon, France

    Abd Errahmane Kiouche & Hamida Seba

Authors
  1. Aymen Ourdjini
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  2. Abd Errahmane Kiouche
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  3. Hamida Seba
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Corresponding author

Correspondence to Hamida Seba .

Editor information

Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Mario Vento

  2. University of Salerno, Fisciano, Italy

    Pasquale Foggia

  3. University of Tours, Tours, France

    Donatello Conte

  4. University of Salerno, Fisciano, Italy

    Vincenzo Carletti

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Ourdjini, A., Kiouche, A.E., Seba, H. (2023). An Efficient Entropy-Based Graph Kernel. In: Vento, M., Foggia, P., Conte, D., Carletti, V. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2023. Lecture Notes in Computer Science, vol 14121. Springer, Cham. https://doi.org/10.1007/978-3-031-42795-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-42795-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42794-7

  • Online ISBN: 978-3-031-42795-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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