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The rise and fall of countries on world trade web: A network perspective

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  • Tianlong Fan

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, P. R. China†Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001, P. R. China‡Department of Physics, University of Fribourg, Fribourg 1700, Switzerland)

  • Hao Li

    (#xA7;College of Engineering, Northeastern University, Boston 02115, US)

  • Xiao-Long Ren

    (#x2020;Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001, P. R. China¶Computational Social Science, ETH Zürich, Zürich 8092, Switzerland)

  • Shuqi Xu

    (#x2020;Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001, P. R. China∥Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China)

  • Youzhao Gou

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, P. R. China)

  • Linyuan Lü

    (#x2020;Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001, P. R. China∥Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China**Beijing Computational Science Research Center, Beijing 100193, P. R. China)

Abstract

World Trade Web is the backbone of the global economy system. Identifying influential countries and regions in such a network and revealing their importance evolution over time are helpful for understanding global economic development. Here, we collect the worldwide trade data in commodities of 232 countries and regions from 1996 to 2015 from the UN Comtrade Database, based on which a series of weighted world trade networks are constructed. Since the networks are almost fully connected, most of the existing methods may fail in identifying the important nodes. To tackle this issue, we apply the generalized Degree, H-index and Coreness (DHC) theorem to the constructed networks and use weighted degree and coreness to quantify nodes’ importance, since they can make full use of the weight information to accurately evaluate nodes’ significance. Then, we analyze the rankings of countries and regions measured by various indicators, whose differences and advantages are also compared. We further present the evolution of countries’ significance over time, two typical groups of countries. The results show that the influence of a country or region has a strong correlation with its economic scale, but a relatively weak correlation with the diversity of its trade structure. Finally, based on the findings, we put forward corresponding strategies to enhance the trade influence for different types of countries.

Suggested Citation

  • Tianlong Fan & Hao Li & Xiao-Long Ren & Shuqi Xu & Youzhao Gou & Linyuan Lü, 2021. "The rise and fall of countries on world trade web: A network perspective," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(08), pages 1-19, August.
    • Handle: RePEc:wsi:ijmpcx:v:32:y:2021:i:08:n:s0129183121501217
    DOI: 10.1142/S0129183121501217
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    Cited by:

    1. Ye, Yucheng & Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan, 2022. "Forecasting countries' gross domestic product from patent data," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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