Complex networks and deep learning for copper flow across countries

Lorenzo Federico (), Ayoub Mounim (), Pierpaolo D’Urso () and Livia De Giovanni ()
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Lorenzo Federico: Luiss University - Viale Romania
Ayoub Mounim: Luiss University - Viale Romania
Pierpaolo D’Urso: Sapienza - University of Rome
Livia De Giovanni: Luiss University - Viale Romania

Annals of Operations Research, 2024, vol. 339, issue 1, No 35, 937-963

Abstract: Abstract In this paper, by using a lifecycle perspective, four stages related to the extraction, refining and processing of copper were identified. The different behaviors of countries in the import/export networks at the four stages synthetically reflect their position in the global network of copper production and consumption. The trade flows of four commodities related to the extraction, refining and processing of copper of 142 nations with population above 2 millions based on the UN Comtrade website ( https://comtrade.un.org/data/ ), in five years from 2017 to 2021, were considered. The observed trade flows in each year have been modelled as a directed multilayer network. Then the countries have been grouped according to their structural equivalence in the international copper flow by using a Multilayer Stochastic Block Model. To put further insight in the obtained community structure of the countries, a deep learning model based on adapting the node2vec to a multilayer setting has been used to embed the countries in an Euclidean plane. To identify groups of nations that play the same role across time, some distances between the parameters obtained in consecutive years were introduced. We observe that 97 countries out of 142 consistently occupy the same position in the copper supply chain throughout the five years, while the other 45 move through different roles in the copper supply chain.

Keywords: Social networks; Complex networks; Supply chain; Import/export networks; Trade flows; Community detection; Multilayer Stochastic block model; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05419-x

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