A network analysis of countries' export flows: firm grounds for the building blocks of the economy
Guido Caldarelli, Matthieu Cristelli, Andrea Gabrielli, Luciano, Pietronero, Antonio Scala, Andrea Tacchella

TL;DR
This paper analyzes the global trade network using bipartite graphs of countries and products, revealing socio-geographic clusters, proposing a new classification method, and improving ranking algorithms for countries and products.
Contribution
It introduces a novel filtering method for network analysis, reformulates the reflections method as a fixpoint problem, and proposes a biased Markov chain approach for consistent ranking.
Findings
Country clusters reveal socio-geographic links.
Product clustering enables bottom-up classification.
Non-linear interactions suggest new ranking algorithms.
Abstract
In this paper we analyze the bipartite network of countries and products from UN data on country production. We define the country-country and product-product projected networks and introduce a novel method of filtering information based on elements' similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we mathematically reformulate the "reflections method" introduced by Hidalgo and Hausmann as a fixpoint problem; such formulation highlights some conceptual weaknesses of the approach. To overcome such an issue, we introduce an alternative methodology (based on biased Markov chains) that allows to rank countries in a conceptually consistent way. Our analysis uncovers a strong…
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