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. 2020 Oct 22;10:18046. doi: 10.1038/s41598-020-74964-3

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Scheme representing the pipeline of the analysis. A portion of WDI database^17,76 is selected according to availability criteria. A complex network describing similarity of countries is obtained from pairwise Pearson correlations between their WDIs, after rescaling each WDI in [0, 1]. Community detection results are compared with index rankings with resolution ratio $R > 1$ , in order to evaluate performances of countries according to their development status. Maps are generated with the “Map of Countries” and “GeoLayout” plugins of Gephi 0.9.2⁶².