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Spreading Effect in Industrial Complex Network Based on Revised Structural Holes Theory

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  • Lizhi Xing
  • Qing Ye
  • Jun Guan

Abstract

This paper analyzed the spreading effect of industrial sectors with complex network model under perspective of econophysics. Input-output analysis, as an important research tool, focuses more on static analysis. However, the fundamental aim of industry analysis is to figure out how interaction between different industries makes impacts on economic development, which turns out to be a dynamic process. Thus, industrial complex network based on input-output tables from WIOD is proposed to be a bridge connecting accurate static quantitative analysis and comparable dynamic one. With application of revised structural holes theory, flow betweenness and random walk centrality were respectively chosen to evaluate industrial sectors’ long-term and short-term spreading effect process in this paper. It shows that industries with higher flow betweenness or random walk centrality would bring about more intensive industrial spreading effect to the industrial chains they stands in, because value stream transmission of industrial sectors depends on how many products or services it can get from the other ones, and they are regarded as brokers with bigger information superiority and more intermediate interests.

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  • Lizhi Xing & Qing Ye & Jun Guan, 2016. "Spreading Effect in Industrial Complex Network Based on Revised Structural Holes Theory," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:plo:pone00:0156270
    DOI: 10.1371/journal.pone.0156270
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    Cited by:

    1. 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.
    2. Stefano Costa & Federico Sallusti & Claudio Vicarelli, 2022. "Trade networks and shock transmission capacity: a new taxonomy of Italian industries," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(1), pages 133-153, March.
    3. Xing, Lizhi & Dong, Xianlei & Guan, Jun, 2017. "Global industrial impact coefficient based on random walk process and inter-country input–output table," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 576-591.
    4. Jun Guan & Xiaoyu Xu & Shan Wu & Lizhi Xing, 2018. "Measurement and simulation of the relatively competitive advantages and weaknesses between economies based on bipartite graph theory," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-28, May.
    5. Xing, Lizhi & Guan, Jun & Dong, Xianlei & Wu, Shan, 2018. "Understanding the competitive advantage of TPP-related nations from an econophysics perspective: Influence caused by China and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 164-184.
    6. Weidong Li & Anjian Wang & Weiqiong Zhong & Chunhui Wang, 2022. "An Impact Path Analysis of Russo–Ukrainian Conflict on the World and Policy Response Based on the Input–Output Network," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    7. Xing, Lizhi & Guan, Jun & Wu, Shan, 2018. "Measuring the impact of final demand on global production system based on Markov process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 148-163.
    8. Xing, Lizhi & Wang, Dawei & Li, Yan & Guan, Jun & Dong, Xianlei, 2020. "Simulation analysis of the competitive status between China and Portuguese-speaking countries under the background of one belt and one road initiative," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

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