Analysis of risk propagation using the world trade network

被引:0
|
作者
Sungyong Kim
Jinhyuk Yun
机构
[1] Soongsil University,School of AI Convergence
来源
Journal of the Korean Physical Society | 2022年 / 81卷
关键词
World trade network; Personalized PageRank; Econophysics; Complex systems;
D O I
暂无
中图分类号
学科分类号
摘要
An economic system is an exemplar of a complex system in which all agents interact simultaneously. Interactions between countries have generally been studied using the flow of resources across diverse trade networks, in which the degree of dependence between two countries is typically measured based on the trade volume. However, indirect influences may not be immediately apparent. Herein, we compared a direct trade network to a trade network constructed using the personalized PageRank (PPR) encompassing indirect influences. By analyzing the correlation of the gross domestic product (GDP) between countries, we discovered that the PPR trade network has greater explanatory power on the propagation of economic events than direct trade by analyzing the GDP correlation between countries. To further validate our observations, an agent-based model of the spreading economic crisis was implemented for the Russia–Ukraine war of 2022. The model also demonstrates that the PPR explains the actual impact more effectively than the direct trade network. Our research highlights the significance of indirect and long-range relationships, which have often been overlooked.
引用
收藏
页码:697 / 706
页数:9
相关论文
共 50 条
  • [31] A Network Analysis of World Trade Structural Changes (1996-2019)
    Hoang, Vu Phuong
    Piccardi, Carlo
    Tajoli, Lucia
    COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 1, 2023, 1077 : 490 - 501
  • [32] Analyzing Global Geopolitical Stability in Terms of World Trade Network Analysis
    Papadopoulos, Georgios D.
    Magafas, Lykourgos
    Demertzis, Konstantinos
    Antoniou, Ioannis
    INFORMATION, 2023, 14 (08)
  • [33] Research on Stock Network Modeling and Risk propagation based on Complex Network Analysis
    Jin, Xin
    Wu, Ying
    Jin, Chu
    PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016), 2016, : 1188 - 1194
  • [34] Structure and evolution of the world trade network
    Garlaschelli, D
    Loffredo, MI
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2005, 355 (01) : 138 - 144
  • [35] Google Matrix of the World Trade Network
    Ermann, L.
    Shepelyansky, D. L.
    ACTA PHYSICA POLONICA A, 2011, 120 (6A) : A158 - A171
  • [36] Crisis contagion in the world trade network
    Célestin Coquidé
    José Lages
    Dima L. Shepelyansky
    Applied Network Science, 5
  • [37] The architecture of weighted world trade network
    Liu Bao-quan
    Ji Jian-hua
    Duan Wen-qi
    PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (14TH) VOLS 1-3, 2007, : 1260 - +
  • [38] Opinion Formation in the World Trade Network
    Coquide, Celestin
    Lages, Jose
    Shepelyansky, Dima L.
    ENTROPY, 2024, 26 (02)
  • [39] Crisis contagion in the world trade network
    Coquide, Celestin
    Lages, Jose
    Shepelyansky, Dima L.
    APPLIED NETWORK SCIENCE, 2020, 5 (01)
  • [40] Structure and Response in the World Trade Network
    He, Jiankui
    Deem, Michael W.
    PHYSICAL REVIEW LETTERS, 2010, 105 (19)