  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 the 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 modularity 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.
