Community structure in the World Trade Network based on communicability distances
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Community structure in the World Trade Network based on communicability distances Paolo Bartesaghi1
· Gian Paolo Clemente2 · Rosanna Grassi1
Received: 14 January 2020 / Accepted: 17 November 2020 © The Author(s) 2020
Abstract 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. Keywords Network analysis · Communicability distance · Community detection · World Trade Network JEL Classification G65 · D57 · F40
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Paolo Bartesaghi [email protected] Gian Paolo Clemente [email protected] Rosanna Grassi [email protected]
1
Department of Statistics and Quantitative Methods, University of Milano - Bicocca, Via Bicocca degli Arcimboldi, 8, 20126 Milan, Italy
2
Department of Mathematics for Economic, Financial and Actuarial Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
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1 Introduction International trade is based on a set of complex relationships between different countries. Both connections between countries and bilateral trade flows can be modelled as a dense network of interrelated and interconnected agents. A long-standing problem in this field is the detection of communities, namely subset of nodes among which the interactions are stronger than average. Indeed, the community structure of a network reveals how it is internally organized, highlighting the presence of special relationships between nodes, that might not be revealed by direct empirical analyses. In this framework, a specific role is assumed by the distance between nodes. Indeed, the neighbours of a given node are immediately connected to such a node and they can affect its status most directly. Nonetheless, more distant nodes can influence this node while passing through intermediary ones. In the economic field, a network perspective is actually based on the idea that indirect trade relationships may be important (see, for example, Fagiolo et al. 2015). For instance, Abeysinghe and Forbes (2005) explain the impact of shocks on a given country by indirect trade links. Based on a global VaR approach, Dées and Saint-Guilhem (2011) show that countries that do
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