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Understanding the structure of communities in a network has a greatimportance in the economic analysis. Communities are indeed characterized byspecific properties, that are different from those of both the individual nodeand the whole network, and they can affect various processes on the network. Inthe International Trade Network, community detection aims to search sets ofcountries (or of trade sectors) which have a high intra-cluster connectivityand a low inter-cluster connectivity. In general, exchanges among countriesoccur according to preferential economic relationships ranging over differentsectors. In this paper, we combine community detection with specifictopological indicators, such as centrality measures. As a result, a newweighted network is constructed by the original one, in which weights aredetermined taking into account all the topological indicators in amulti-criteria approach. To solve the resulting Clique Partitioning Problem andfind homogeneous group of nations, we use a new fast algorithm, based on quickdescents to a local optimal solution. The analysis allows to cluster countriesby interconnections, economic power and intensity of trade, giving an importantoverview on the international trade patterns.

Multi-attribute community detection in International Trade Network