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Dominance Network Analysis: Hybridizing Dea and Complex Networks for Data Analytics

In: Data-Enabled Analytics

Author

Listed:
  • L. Calzada-Infante

    (Universidad de León)

  • S. Lozano

    (University of Seville)

Abstract

Although originated in the efficiency analysis realm, Data Envelopment Analysis (DEA) is a data-driven non-parametric methodology that can be effectively used for data analytics of multidimensional datasets. This is particularly so when hybridized with complex network analysis tools. One such hybridization is Dominance Network Analysis, which is based on representing the data through the dominance relationships between the observations. The analysis assumes that the variables are either positive or negative and that we are interested in benchmarking the observations against the observed best practice. The network paradigm provides a versatile and efficient modelling framework that allows computing a wide array of quantitative characterization measures as well as powerful visualization capabilities. The methodology is illustrated with data on how the COVID-19 pandemic has affected the different countries.

Suggested Citation

  • L. Calzada-Infante & S. Lozano, 2021. "Dominance Network Analysis: Hybridizing Dea and Complex Networks for Data Analytics," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 231-262, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-75162-3_9
    DOI: 10.1007/978-3-030-75162-3_9
    as

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