Literature DB >> 22181238

Randomizing world trade. II. A weighted network analysis.

Tiziano Squartini1, Giorgio Fagiolo, Diego Garlaschelli.   

Abstract

Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of the binary projections of the ITN by considering its weighted representations. We show that, unlike the binary case, all possible weighted representations of the ITN (directed and undirected, aggregated and disaggregated) cannot be traced back to local country-specific properties, which are therefore of limited informativeness. Our two papers show that traditional macroeconomic approaches systematically fail to capture the key properties of the ITN. In the binary case, they do not focus on the degree sequence and hence cannot characterize or replicate higher-order properties. In the weighted case, they generally focus on the strength sequence, but the knowledge of the latter is not enough in order to understand or reproduce indirect effects.

Year:  2011        PMID: 22181238     DOI: 10.1103/PhysRevE.84.046118

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  8 in total

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2.  Randomizing bipartite networks: the case of the World Trade Web.

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Journal:  Sci Rep       Date:  2015-06-01       Impact factor: 4.379

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5.  Insights into countries' exposure and vulnerability to food trade shocks from network-based simulations.

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Journal:  Sci Rep       Date:  2022-03-17       Impact factor: 4.379

6.  Measuring the intangibles: a metrics for the economic complexity of countries and products.

Authors:  Matthieu Cristelli; Andrea Gabrielli; Andrea Tacchella; Guido Caldarelli; Luciano Pietronero
Journal:  PLoS One       Date:  2013-08-05       Impact factor: 3.240

7.  Hubs and authorities in the world trade network using a weighted HITS algorithm.

Authors:  Tsuyoshi Deguchi; Katsuhide Takahashi; Hideki Takayasu; Misako Takayasu
Journal:  PLoS One       Date:  2014-07-22       Impact factor: 3.240

8.  Temporal efficiency evaluation and small-worldness characterization in temporal networks.

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Journal:  Sci Rep       Date:  2016-09-29       Impact factor: 4.379

  8 in total

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