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.

Grassi, R., Bartesaghi, P., Benati, S., Clemente, G. P., Multi-Attribute Community Detection in International Trade Network, <<NETWORKS AND SPATIAL ECONOMICS>>, N/A; (N/A): N/A-N/A. [doi:10.1007/s11067-021-09547-4] [http://hdl.handle.net/10807/183381]

Multi-Attribute Community Detection in International Trade Network

Clemente, Gian Paolo
2021

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.
2021
Inglese
Grassi, R., Bartesaghi, P., Benati, S., Clemente, G. P., Multi-Attribute Community Detection in International Trade Network, <<NETWORKS AND SPATIAL ECONOMICS>>, N/A; (N/A): N/A-N/A. [doi:10.1007/s11067-021-09547-4] [http://hdl.handle.net/10807/183381]
File in questo prodotto:
File Dimensione Formato  
NETS2021_Article_Multi-AttributeCommunityDetect.pdf

accesso aperto

Tipologia file ?: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 6.03 MB
Formato Adobe PDF
6.03 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/183381
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact