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Air Transport Network: A Comparison of Statistical Backbone Filtering Techniques

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Complex Networks and Their Applications XI (COMPLEX NETWORKS 2016 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1078))

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Abstract

The big break in data collection tools of large-scale networks from biological, social, and technological domains expands the challenge of their visualization and processing. Numerous structural and statistical backbone extraction techniques aim to reduce the network’s size while preserving its gist. Here, we perform an experimental comparison of seven main statistical methods in an air transportation case study. Correlations analysis shows that Marginal Likelihood Filter (MLF), Locally Adaptive Network Sparsification Filter (LANS), and Disparity Filter are biased toward high weighted edges. We compare the extracted backbones using four indicators: the size of the largest component, the number of nodes, edges, and the total weight. Results show that techniques based on a binomial distribution null model (MLF and Noise Corrected Filter) tend to retain many edges. Conversely, Disparity Filter, Polya Urn Filter, LANS Filter, and Global Statistical Significance Filter (GLOSS) are pretty aggressive in filtering edges. The ECM Filter lies between these two behaviors. These results may guide users in selecting appropriate techniques for their applications.

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Acknowledgements

This material is based upon work supported by the Agence Nationale de Recherche under grant ANR-20-CE23-0002.

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Authors and Affiliations

  1. Laboratoire d’Informatique de Bourgogne, University of Bourgogne, Franche-Comté, Dijon, France

    Ali Yassin, Hocine Cherifi & Olivier Togni

  2. University of Lyon, UCBL, CNRS, INSA Lyon, LIRIS UMR5205, 69622, Villeurbanne, France

    Hamida Seba

Authors
  1. Ali Yassin
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  2. Hocine Cherifi
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  3. Hamida Seba
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  4. Olivier Togni
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Corresponding author

Correspondence to Ali Yassin .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon, France

    Hocine Cherifi

  2. Dipartimento di Fisica e Chimica—Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Rosario Nunzio Mantegna

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

    Luis M. Rocha

  4. IUT Lumière—Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  5. Dipartimento di Fisica e Chimica—Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Salvatore Micciche

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Yassin, A., Cherifi, H., Seba, H., Togni, O. (2023). Air Transport Network: A Comparison of Statistical Backbone Filtering Techniques. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_43

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  • DOI: https://doi.org/10.1007/978-3-031-21131-7_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21130-0

  • Online ISBN: 978-3-031-21131-7

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