Multi-Attribute Community Detection in International Trade Network

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.

Environmentally extended input-output analysis in complex networks: a multilayer approach

“…Furthermore, we notice that relevant sectors, based on multilayers hubs and scores and reported in Table 1, play a crucial role for these countries (see, for instance, node EGW in the three networks). Based on broadcasting and receiving rankings, it could be observed that the topranked countries are representative of three largest clusters detected by community detection methods on the international trade network (see, for example, [2], [6], [13], [16], [18], [20], [39] and [42]): the American cluster, the Pacific cluster and the European cluster. In addition to the information mined by the mesoscale structure, the proposed approach highlights the pivotal countries of each community.…”

Most influential countries in the international medical device trade: Network-based analysis

“…Previous studies based on complex network theory have focused mainly on the structural characteristics and evolution patterns of the trade network from many aspects, such as detecting trade communities formed by some countries with tight trade relationships [26] , [27] , [28] , analyzing the structure of the core and periphery [29] , evaluating the centrality of countries [25] , [30] , identifying the relationships between the centrality of countries and other country characteristics [30] , [31] , estimating or predicting trade flows in the trade network [32] , [33] , [34] , revealing the impact factors of trade network formation [27] , [29] , investigating changes in structural characteristics during the evolution [35] , [36] , exploring risk transmission in the trade network [37] , [38] , and identifying the robustness of the trade network [39] , [40] .…”