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Multi-Attribute Community Detection in International Trade Network
Networks and Spatial Economics ( IF 2.4 ) Pub Date : 2021-07-14 , DOI: 10.1007/s11067-021-09547-4
Rosanna Grassi 1 , Paolo Bartesaghi 1 , Stefano Benati 2 , Gian Paolo Clemente 3
Affiliation  

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.



中文翻译:

国际贸易网络中的多属性社区检测

了解网络中社区的结构在经济分析中具有重要意义。社区确实具有特定的特性,不同于单个节点和整个网络的特性,它们可以影响网络上的各种进程。在国际贸易网络中,社区检测旨在搜索具有高集群内连通性和低集群间连通性的国家(或贸易部门)集。一般来说,国家之间的交流是根据不同部门的优惠经济关系而发生的。在本文中,我们将社区检测与特定的拓扑指标(例如中心性度量)相结合。结果,从原始网络构建了一个新的加权网络,其中权重的确定考虑了多标准方法中的所有拓扑指标。为了解决由此产生的集团划分问题并找到同质的国家集团,我们使用了一种新的快速算法,基于快速下降到局部最优解。该分析允许通过相互关联、经济实力和贸易强度对国家进行聚类,从而对国际贸易模式进行重要概述。

更新日期:2021-07-14
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