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Grand canonical validation of the bipartite international trade network

Overview of attention for article published in arXiv, August 2017
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4 tweeters

Citations

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6 Dimensions

Readers on

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24 Mendeley
Title
Grand canonical validation of the bipartite international trade network
Published in
arXiv, August 2017
DOI 10.1103/physreve.96.022306
Pubmed ID
Authors

Mika J. Straka, Guido Caldarelli, Fabio Saracco

Abstract

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Researcher 5 21%
Professor 3 13%
Student > Master 2 8%
Lecturer > Senior Lecturer 1 4%
Other 4 17%
Unknown 2 8%
Readers by discipline Count As %
Physics and Astronomy 8 33%
Mathematics 4 17%
Social Sciences 3 13%
Computer Science 2 8%
Economics, Econometrics and Finance 2 8%
Other 2 8%
Unknown 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2017.
All research outputs
#8,403,453
of 14,555,594 outputs
Outputs from arXiv
#168,105
of 564,948 outputs
Outputs of similar age
#131,434
of 258,666 outputs
Outputs of similar age from arXiv
#6,152
of 18,354 outputs
Altmetric has tracked 14,555,594 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564,948 research outputs from this source. They receive a mean Attention Score of 3.8. This one has gotten more attention than average, scoring higher than 65% of its peers.
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We're also able to compare this research output to 18,354 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.