In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

在本文中，我们研究了世界贸易网络的中尺度结构。在此框架中，特定作用是由短距离和远程交互作用，因此由国家之间任何适当定义的基于网络的距离承担。因此，我们通过一种利用Estrada通讯距离和振动通讯距离的新程序来识别集群，事实证明它们特别适合捕获经济网络的内部结构。所提出的方法旨在找到使为通用度量空间定义的特定质量函数最大化的距离阈值。主要优点涉及该过程的计算效率以及检查所生成社区的集群间和集群内属性的可能性。