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Complex network analysis on provincial innovation development in China

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  • Yu, Guihai
  • Wan, Yibo
  • Jiang, Caoqing

Abstract

From the perspective of innovation output, the innovation output networks of 30 provinces (excluding Tibert, Hong Kong, Macao and Taiwan) are constructed based on the modified gravity model in the past 20 years (2001–2020). Combined with the method of principal component analysis and TOPSIS, the innovation development level of each province and seven regions is evaluated by integrating some node statistical indicators. Finally, we employ the temporal exponential random graph model to study the main characteristics that affect the evolution of China’s innovation capability in the past 20 years. The conclusions are as follows: (1) There are reciprocity effect, hierarchy effect and time dependence effect in China’s innovation linkages. There is a positive correlation between innovation output and innovation absorption. (2) In the past 20 years China’s innovation output has shown a pattern of strong in the south and weak in the north, strong in the east and weak in the west. The innovation development of each province and region has changed greatly, and the gap is obvious. At present, the national innovation pattern has formed a big cycle in the eastern, central and western regions. East China and south central regions are the regional centers of innovation output in China; Southwest China is a region with rapid innovation and development; Northwest China has been isolated. (3) Provinces in the same region are likely to have innovation linkages with provinces with coexisting economic output effects. (4) The introduction of foreign capital technology promotes China’s independent innovation capability. (5) The intensity of government investment in scientific and technological research and development has ensured the orderly progress of innovation and stabilized innovation capacity. (6) Talents and education level determine the upper limit of China’s innovation ability. (7) Economic level and innovation ability complement each other.

Suggested Citation

  • Yu, Guihai & Wan, Yibo & Jiang, Caoqing, 2023. "Complex network analysis on provincial innovation development in China," Applied Mathematics and Computation, Elsevier, vol. 455(C).
    • Handle: RePEc:eee:apmaco:v:455:y:2023:i:c:s0096300323002722
    DOI: 10.1016/j.amc.2023.128103
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    References listed on IDEAS

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    1. Muhammed Dalgin & Vitor Trindade & Devashish Mitra, 2008. "Inequality, Nonhomothetic Preferences, and Trade: A Gravity Approach," Southern Economic Journal, John Wiley & Sons, vol. 74(3), pages 747-774, January.
    2. Muhammed Dalgin & Vitor Trindade & Devashish Mitra, 2008. "Inequality, Nonhomothetic Preferences, and Trade: A Gravity Approach," Southern Economic Journal, John Wiley & Sons, vol. 74(3), pages 747-774, January.
    3. Mario A. Maggioni & T. Erika Uberti, 2007. "Inter-regional Knowledge Flows in Europe: An Econometric Analysis," Chapters, in: Koen Frenken (ed.), Applied Evolutionary Economics and Economic Geography, chapter 11, Edward Elgar Publishing.
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    Cited by:

    1. Minghua Chen & Qian Li & Bianxiu Zhang & Linxiao Xie & Jianxu Liu & You Geng & Zhirui Liu, 2023. "The Spatial Correlation Network of China’s High-Quality Development and Its Driving Factors," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
    2. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

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