Structure and Response in the World Trade Network

He, Jiankui; Deem, Michael W.

doi:10.1103/physrevlett.105.198701

Cited by 91 publications

(75 citation statements)

References 17 publications

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“…In our recent work on NaFeAs [6], the ARPES data show very sharp features compared with those of other iron pnictides. Furthermore, three characteristic phase transitions of iron pnictides (the structural, spin density wave, and superconducting transitions) all occur in NaFeAs.…”

Section: Introductionmentioning

confidence: 83%

The orbital characters and k dispersions of bands in iron-pnictide NaFeAs

He¹,

Zhang²,

Wang³

et al. 2011

Journal of Physics and Chemistry of Solids

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Section: Introductionmentioning

confidence: 83%

The orbital characters and k dispersions of bands in iron-pnictide NaFeAs

He¹,

Zhang²,

Wang³

et al. 2011

Journal of Physics and Chemistry of Solids

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“…More recently the interest to the analysis of the world trade as a network becomes more and more pronounced with a few publications in this area [1623]. Thus, the global network characteristics were considered in [16,17], degree centrality measures were analyzed in [18,19] and time evolution of network global characteristics was studied in [20]. Topological and clustering properties of trade network and multinetwork of various commodities were discussed in [19,21,22].…”

Section: Introductionmentioning

confidence: 99%

Google Matrix of the World Trade Network

Ermann

Shepelyansky

2011

Acta Phys. Pol. A

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Using the United Nations Commodity Trade Statistics Database we construct the Google matrix of the world trade network and analyze its properties for various trade commodities for all countries and all available years from 1962 to 2009. The trade ows on this network are classied with the help of PageRank and CheiRank algorithms developed for the World Wide Web and other large scale directed networks. For the world trade this ranking treats all countries on equal democratic grounds independent of country richness. Still this method puts at the top a group of industrially developed countries for trade in all commodities. Our study establishes the existence of two solid state like domains of rich and poor countries which remain stable in time, while the majority of countries are shown to be in a gas like phase with strong rank uctuations. A simple random matrix model provides a good description of statistical distribution of countries in two-dimensional rank plane. The comparison with usual ranking by export and import highlights new features and possibilities of our approach.

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“…The ITN is close to the fully connected network because a country trades to almost all countries. The analysis of the ITN was based on these densely connected networks 18,19,20,21,22,23,24,25,26,27,28,29 .…”

Section: Introductionmentioning

confidence: 99%

Complex Networks and Minimal Spanning Trees in International Trade Network

Maeng

Choi

Lee

2012

Int. J. Mod. Phys. Conf. Ser.

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The wealth of a nation is changed by the internal economic growth of a nation and by the international trade among countries. Trade between countries are one of their most important interactions and thus expects to affect crucially the wealth distribution over countries. We reviewed the network properties of the international trade networks (ITN). We analyzed data sets of world trade. The data set include a total number of 190 countries from 1950 to 2000. We observed that the world trade network showed the uneven trading relationships which are measured by the disparity. The effective disparity followed a power law, < D(k) >∼ t δ , for the import and export network. We also construct the minimal spanning tree(MST) of international trade network, where each node is a country and directed links connecting them represent money flow from a source node to a target one. The topology of the MST shows the flow patterns of the international trades. From the MST we can identify the sub-economic zone if we delete the hub node. We observed that the cumulative degree distribution functions follow the power law, P>(k) ∼ k −α , with the average exponent α = 1.1(1)). We also calculated the betweenness centrality(BC) of the MST. The cumulative probability distribution of the betweenness centrality follows the power law, P>(BC) ∼ BC −β , with the average exponent β = 1.09(7).

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Structure and Response in the World Trade Network

Cited by 91 publications

References 17 publications

The orbital characters and k dispersions of bands in iron-pnictide NaFeAs

The orbital characters and k dispersions of bands in iron-pnictide NaFeAs

Google Matrix of the World Trade Network

Complex Networks and Minimal Spanning Trees in International Trade Network

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