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

L. Calzada-Infante () and S. Lozano ()
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L. Calzada-Infante: Universidad de León
S. Lozano: University of Seville

A chapter in Data-Enabled Analytics, 2021, pp 231-262 from Springer

Abstract: 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.

Keywords: Data envelopment analytics; Complex network analysis; Dominance network; 3Vs; COVID-19 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-75162-3_9

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DOI: 10.1007/978-3-030-75162-3_9

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