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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
22 tweeters

Citations

dimensions_citation
17 Dimensions

Readers on

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

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 22 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 3%
Italy 1 3%
China 1 3%
Unknown 31 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 10 29%
Student > Master 2 6%
Professor 2 6%
Lecturer 1 3%
Other 2 6%
Unknown 7 21%
Readers by discipline Count As %
Economics, Econometrics and Finance 7 21%
Social Sciences 5 15%
Unspecified 4 12%
Physics and Astronomy 4 12%
Business, Management and Accounting 2 6%
Other 5 15%
Unknown 7 21%

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
#825,985
of 11,467,880 outputs
Outputs from PLoS ONE
#15,355
of 127,342 outputs
Outputs of similar age
#17,184
of 186,311 outputs
Outputs of similar age from PLoS ONE
#537
of 3,805 outputs
Altmetric has tracked 11,467,880 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 127,342 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. 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 186,311 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 3,805 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.