Multi-Attribute Community Detection in International Trade Network

Rosanna Grassi (), Paolo Bartesaghi, Stefano Benati and Gian Paolo Clemente
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Rosanna Grassi: University of Milano - Bicocca
Paolo Bartesaghi: University of Milano - Bicocca
Stefano Benati: University of Trento
Gian Paolo Clemente: Università Cattolica del Sacro Cuore

Networks and Spatial Economics, 2021, vol. 21, issue 3, No 8, 707-733

Abstract: Abstract Understanding the structure of communities in a network has a great importance in the economic analysis. Communities are indeed characterized by specific properties, that are different from those of both the individual nodes and the whole network, and they can affect various processes on the network. In the International Trade Network, community detection aims to search sets of countries (or of trade sectors) which have a high intra-cluster connectivity and a low inter-cluster connectivity. In general, exchanges among countries occur according to preferential economic relationships ranging over different sectors. In this paper, we combine community detection with specific topological indicators, such as centrality measures. As a result, a new weighted network is constructed from the original one, in which weights are determined taking into account all the topological indicators in a multi-criteria approach. To solve the resulting Clique Partitioning Problem and find homogeneous group of nations, we use a new fast algorithm, based on quick descents to a local optimal solution. The analysis allows to cluster countries by interconnections, economic power and intensity of trade, giving an important overview on the international trade patterns.

Keywords: Networks; Community detection; Centrality measures; International Trade Network; CP-problem (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11067-021-09547-4

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