Community Structure in the Multi-network of International Trade

We study the community structure of the multi-network of commodity-specific and aggregate trade relations among world countries over the 1992-2003 period. We compare structures across products and time by means of the normalized mutual information index (NMI). We also compare them with exogenous community structures induced by geographical distances and regional trade agreements. We find that: (i) plastics and mineral fuels —and in general commodities belonging to the chemical sector— have the highest similarity with aggregate trade communities; (ii) both at aggregated and disaggregated levels, physical variables such as geographical distance are more correlated with the observed trade fluxes than regional-trade agreements.

[1]  Giorgio Fagiolo,et al.  World-trade web: topological properties, dynamics, and evolution. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[3]  Giorgio Fagiolo,et al.  On the Topological Properties of the World Trade Web: A Weighted Network Analysis , 2007, 0708.4359.

[4]  S. S. Manna,et al.  The International Trade Network , 2007, 0707.4347.

[5]  K. Kaski,et al.  The International Trade Network: weighted network analysis and modelling , 2007, 0707.4343.

[6]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[7]  Giorgio Fagiolo,et al.  The international-trade network: gravity equations and topological properties , 2009, 0908.2086.

[8]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[9]  Sergio Gómez,et al.  Size reduction of complex networks preserving modularity , 2007, ArXiv.

[10]  Marián Boguñá,et al.  Patterns of dominant flows in the world trade web , 2007, 0704.1225.

[11]  Diego Garlaschelli,et al.  Fitness-dependent topological properties of the world trade web. , 2004, Physical review letters.

[12]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[14]  Giorgio Fagiolo,et al.  Assessing the Evolution of International Economic Integration Using Random Walk Betweenness Centrality: the Cases of East Asia and Latin America , 2008, Adv. Complex Syst..

[15]  R. Mantegna.  Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[16]  César A. Hidalgo,et al.  The building blocks of economic complexity , 2009, Proceedings of the National Academy of Sciences.

[17]  Daniel N. Rockmore,et al.  Evolution of community structure in the world trade web , 2008 .

[18]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[19]  César A. Hidalgo,et al.  The Product Space Conditions the Development of Nations , 2007, Science.

[20]  T. Aste,et al.  Interplay between topology and dynamics in the World Trade Web , 2007 .

[21]  Guanrong Chen,et al.  Complexity and synchronization of the World trade Web , 2003 .

[22]  Marián Boguñá,et al.  Topology of the world trade web. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Bikas K. Chakrabarti,et al.  Econophysics of Markets and Business Networks : Proceedings of the Econophys-Kolkata III , 2007 .

[24]  Giorgio Fagiolo,et al.  Multinetwork of international trade: a commodity-specific analysis. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Douglas R. White,et al.  Role models for complex networks , 2007, 0708.0958.

[26]  D. Garlaschelli,et al.  Structure and evolution of the world trade network , 2005, physics/0502066.