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.

Alliances and conflicts in social, political and economic relations can be represented by positive and negative edges in signed networks. A cycle is said to be positive if the product of its edge signs is positive, otherwise it is negative. Then, a signed network is balanced if and only if all its cycles are positive. An index characterizing how much a signed network deviates from being balanced is known as a global balance index. Here we give a step forward in the characterization of signed networks by defining a local balance index, which characterizes how much a given vertex of a signed network contributes to its global balance. We analyze the mathematical foundations and unique structural properties of this index. Then, we apply this index to the study of the evolution of international relations in the globe for the period 1816–2014. In this way we detect and categorize major historic events based on balance fluctuations, helping our understanding towards new mixed approaches to history based on network theory.