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Evolution mechanism of industrial network in Yangtze River Delta region from the perspective of link prediction

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  • Yue Shen
  • Yixin Ren
  • Yiwen Zhang

Abstract

The Yangtze River Delta (YRD) is an important engine of national economic development and a leading region in international competition. As economic exchanges and resource flows in the YRD region become closer, the inter-regional industrial linkages continue to grow, resulting in the formation of an industrial network structure characterized by a “complex network”. The strength of the links between industrial sectors and the value and significance of the existence of industries in the network change over time, thus causing the overall evolution of the industrial network in the YRD region. Based on the input-output tables of the YRD region in 2012 and 2017, this paper uses the prediction index of network structure similarity to construct the prediction model of industrial network link between the YRD regions, and calculates the possibility of future links between industries in the Yangtze River Delta region through comparative analysis and selection of the RWR index of random walk similarity with the best effect, and concludes that: (1) the homogeneity of industries among provinces and cities in the Yangtze River Delta region is relatively high, resulting in homogeneous competition; (2) the overall nature of the industrial layout of the YRD is not prominent, and the depth and intensity of cross-regional industrial cooperation are lacking. On the basis of analysis and research, the countermeasures and suggestions for effectively realizing industrial integration are put forward from the macro level of the government and the meso level of the industry, so as to achieve a more complete industrial network in the YRD region and a more extensive length and width of the cross-regional industrial chain.

Suggested Citation

  • Yue Shen & Yixin Ren & Yiwen Zhang, 2024. "Evolution mechanism of industrial network in Yangtze River Delta region from the perspective of link prediction," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-27, September.
    • Handle: RePEc:plo:pone00:0308544
    DOI: 10.1371/journal.pone.0308544
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    References listed on IDEAS

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    1. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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