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Tailoring Benchmark Graphs to Real-World Networks for Improved Prediction of Community Detection Performance

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1142))

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Abstract

Analysts interested in understanding the community structure of a particular real-world network will often simply choose a popular community detection algorithm and trust the generated results without further investigation, but algorithm performance can vary depending on the network characteristics. We demonstrate that by running experiments on benchmark graphs tailored to match characteristics of a real-world network of interest, a better understanding can be obtained on how community detection algorithms will perform on the real-world network. We show that the correlation between the performance of the community detection methods on a publicly available dataset to the average performance of the same methods on the corresponding tailored benchmark graphs is high whereas the correlation with LFR benchmark graphs is negative. This means the methods that performed well on the tailored graphs also performed well on the real-world network but methods that perform well on LFR graphs did not perform well on the real-world network, demonstrating that the proposed methodology has merit.

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Acknowledgments

This paper was written under a Janney Grant from Johns Hopkins University Applied Physics Laboratory. We also thank Carlo V. Cannistraci, who introduced the authors to the nPSO benchmark at the 8th International Conference on Complex Networks and their Applications in 2019.

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Authors and Affiliations

  1. Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA

    Catherine Schwartz, Cetin Savkli & Amanda Galante

  2. University of Maryland Department of Mathematics, College Park, MD, USA

    Catherine Schwartz & Wojciech Czaja

Authors
  1. Catherine Schwartz
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  2. Cetin Savkli
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  3. Amanda Galante
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  4. Wojciech Czaja
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Corresponding author

Correspondence to Catherine Schwartz .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

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Schwartz, C., Savkli, C., Galante, A., Czaja, W. (2024). Tailoring Benchmark Graphs to Real-World Networks for Improved Prediction of Community Detection Performance. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_9

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  • DOI: https://doi.org/10.1007/978-3-031-53499-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53498-0

  • Online ISBN: 978-3-031-53499-7

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