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The Rise of China in the International Trade Network: A Community Core Detection Approach

Overview of attention for article published in PLoS ONE, August 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
21 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
39 Mendeley
Title
The Rise of China in the International Trade Network: A Community Core Detection Approach
Published in
PLoS ONE, August 2014
DOI 10.1371/journal.pone.0105496
Pubmed ID
Authors

Zhen Zhu, Federica Cerina, Alessandro Chessa, Guido Caldarelli, Massimo Riccaboni

Abstract

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Switzerland 1 3%
Italy 1 3%
China 1 3%
Unknown 35 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 33%
Researcher 10 26%
Student > Bachelor 4 10%
Unspecified 4 10%
Student > Master 2 5%
Other 6 15%
Readers by discipline Count As %
Economics, Econometrics and Finance 8 21%
Unspecified 8 21%
Social Sciences 6 15%
Physics and Astronomy 6 15%
Business, Management and Accounting 2 5%
Other 9 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 16 January 2018.
All research outputs
#1,057,735
of 12,910,847 outputs
Outputs from PLoS ONE
#17,873
of 139,780 outputs
Outputs of similar age
#18,478
of 189,904 outputs
Outputs of similar age from PLoS ONE
#564
of 3,856 outputs
Altmetric has tracked 12,910,847 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 139,780 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 189,904 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 3,856 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.