论文标题
大流行对复杂社会经济系统的影响：通过通讯引起的社区检测
The effect of the pandemic on complex socio-economic systems: community detection induced by communicability
论文作者
论文摘要
相互关联的系统的复杂性日益增加使使用多重网络成为解释系统中元素之间关系性质的重要工具。在本文中，我们旨在调查冠状病毒大流行时期国家行为的各个方面。通过一个多重网络，我们考虑同时考虑严格的指数值，COVID-19感染和国际贸易数据，以检测出与大流行反应相似的国家的集群。我们基于埃斯特拉达（Estrada）通讯性提出了一种新的方法论方法，该方法是基于两步优化在多重网络上识别社区的。首先，我们通过最小化距离函数来确定水平之间的最佳层间强度。因此，最佳层间强度用于检测每一层的社区。我们的发现表明，与单层网络的经典方法相比，该多重网络上的社区检测具有更大的信息能力。我们的方法更好地揭示了每层在每个单层上使用相同方法的应用。此外，在多重案例中检测到的组益处具有较高的内聚力，从而相对于在单层案例中获得的较少社区的识别。
The increasing complexity of interrelated systems has made the use of multiplex networks an important tool for explaining the nature of relations between elements in the system. In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network we consider simultaneously stringency index values, COVID-19 infections and international trade data, in order to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization. At first, we determine the optimal inter-layer intensity between levels by minimizing a distance function. Hence, the optimal inter-layer intensity is used to detect communities on each layer. Our findings show that the community detection on this multiplex network has greater information power than classical methods for single-layer networks. Our approach better reveals clusters on each layer with respect to the application of the same approach on each single-layer. Moreover, detected groups in the multiplex case benefit of a higher cohesion, leading to identifying on each layer a lower number of communities with respect to the ones obtained in the single-layer cases.
