期刊
NETWORKS & SPATIAL ECONOMICS
卷 21, 期 3, 页码 707-733出版社
SPRINGER
DOI: 10.1007/s11067-021-09547-4
关键词
Networks; Community detection; Centrality measures; International Trade Network; CP-problem
资金
- Universita degli Studi di Milano -Bicocca within the CRUICARE Agreement
This paper combines community detection with specific topological indicators in the International Trade Network, constructing a new weighted network and using a fast algorithm to find homogeneous groups of nations. This analysis clusters countries based on interconnections, economic power, and intensity of trade, providing insight into international trade patterns.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据