Statistical physics of network communities in economic systems

In the last decade, the study of big networked systems has received a great deal of attention thanks to the increased availability of large datasets and the technology to analyze them. To unravel regularities and behaviours from his enormous quantity of data and supply suitable models, we need appropriate tools, one of them being community detection. Finding meaningful communities in a networks is still a diffcult task but essential to unveil functional relations between the parts. The research presented here has been carried out focusing on community detection; in particular were considered cases where the spatial component was relevant or intrinsic. It is indeed true that, nowadays, many systems, represented as complex networks, are affected, more or less naturally, by the geographical distance, location and organization. This holds true even for economic events: it has been proved that trade and exchanges between countries are necessarily suffocated by the geographical proximity or impeded by natural obstacles. Still, community detection alone is not sufficient to describe the whole picture, since it gives no information about the internal structure of a community. Therefore we developed the novel core detection method, natural counterpart of the community detection algorithm and meant to be performed alongside it, which is, at the same time, simple and powerful. We aim to apply community detection and core detection methodologies to the analysis of the global market and its functioning, in order to understand the origin of economic turmoils and critical events. In this work we analyze different economic systems from a complex network perspective and find some interesting results: we study patent data in order to measure internationalization of European countries and assess the effectiveness of EU policies; we examine the dynamics of network effects on the performances of individual countries and trade relationships in the International Trade Network; we represent World Input-Output data as an interdependent complex network and study its properties, showing evidence of the crisis . Thanks to both community and core detection, we are able to have a deeper insight on the inner workings of community formation, we can identify the leading members in a group and reveal in uence basins, unknown otherwise.