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Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain

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  • Xing, Lizhi
  • Dong, Xianlei
  • Guan, Jun
  • Qiao, Xiaoyong

Abstract

This paper focuses on measuring the globally and nationally economic system’s connectedness and industrial sector’s function on the Global Value Chain (GVC), as reinforcements to the present studies on international trade. Firstly, we reconsidered the length-related and position-related measures in literatures about vertical specialization from the perspective of econophysics. Secondly, we redefined the inter-country and inter-sector propagating process of intermediate goods and proposed the concept of Strongest Relevance Path Length (SRPL) based on Revised Floyd–Warshall Algorithm (RFWA), which is the basis of new measurement. Thirdly, we introduced Average and Maximum Strongest Relevance Degree (ASRD and MSRD) to measure the connectedness and compactness of network respectively. Fourthly, enlightened by betweenness centrality, we introduced SRPL-based index to measure industrial sectors’ Pivotability in transferring intermediate goods Fifthly, these indices were applied to the empirical analysis of the economic system by physical statistics. Finally, through cascading failure analysis, we found that both ASRD and MSRD reflecting the overall performance of propagating process of intermediate goods are vulnerable to those sectors with high pivotability.

Suggested Citation

  • Xing, Lizhi & Dong, Xianlei & Guan, Jun & Qiao, Xiaoyong, 2019. "Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 19-36.
    • Handle: RePEc:eee:phsmap:v:516:y:2019:i:c:p:19-36
    DOI: 10.1016/j.physa.2018.10.004
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    2. Xiao, Shiying & Yan, Jun & Zhang, Panpan, 2022. "Incorporating auxiliary information in betweenness measure for input–output networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Georgios Angelidis & Charalambos Bratsas & Georgios Makris & Evangelos Ioannidis & Nikos C. Varsakelis & Ioannis E. Antoniou, 2021. "Global Value Chains of COVID-19 Materials: A Weighted Directed Network Analysis," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    4. Guan, Jun & Li, Yafei & Xing, Lizhi & Li, Yan & Liang, Guoqiang, 2020. "Closeness centrality for similarity-weight network and its application to measuring industrial sectors’ position on the Global Value Chain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Yelai Feng & Huaixi Wang & Chao Chang & Hongyi Lu, 2022. "Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    6. Zhang, Shuaishuai & Wu, Libo & Zhou, Yang, 2020. "The impact of negative list policy on sectoral structure: Based on complex network and DID analysis," Applied Energy, Elsevier, vol. 278(C).
    7. Li Yang & Dawei Wang & Yuanpeng Ji & Lizhi Xing, 2023. "On the Internal Synergistic Mechanism of Operating System of Beijing’s High-Technology Industry Chain: Evidence from Science and Technology Service Industry," Sustainability, MDPI, vol. 15(3), pages 1-17, January.

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