The international trade naturally maps onto a complex networks. Theoretical analysis of this network gives valuable insights about the global economic system. Although different eco- nomic data sets have been investigated from the network perspective, little attention has been paid to its dynamical behaviour. Here we take the World Input Output Data set, which has values of the annual transactions between 40 different countries of 35 different sectors for the period of 15 years, and infer the time interdependence between countries and sectors. As a measure of interdependence we use correlations between various time series of the net- work characteristics. First we form 15 primary networks for each year of the data we have, where nodes are countries and links are annual exports from one country to the other. Then we calculate the strengths (weighted degree) and PageRank of each country in each of the 15 networks for 15 different years. This leads to sets of time series and by calculating the cor- relations between these we form a secondary network where the links are the positive correla- tions between different countries or sectors. Furthermore, we also form a secondary network where the links are negative correlations in order to study the competition between countries and sectors. By analysing this secondary network we obtain a clearer picture of the mutual influences between countries. As one might expect, we find that political and geographical cir- cumstances play an important role. However, the derived correlation network reveals surpris- ing aspects which are hidden in the primary network. Sometimes countries which belong to the same community in the original network are found to be competitors in the secondary net- works. E.g. Spain and Portugal are always in the same trade flow community, nevertheless secondary network analysis reveal that they exhibit contrary time evolution.


del Río-Chanona, R.M., Grujić, J. & Jensen, H.J. (2017). 'Trends of the World Input and Output Network of Global Trade'. PloS one, 12(1), e0170817.
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