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Research on spatial economic structure for different economic sectors from a perspective of a complex network


  • Hu, Sen
  • Yang, Hualei
  • Cai, Boliang
  • Yang, Chunxia


The economy system is a complex system, and the complex network is a powerful tool to study its complexity. Here we calculate the economic distance matrices based on annual GDP of nine economic sectors from 1995–2010 in 31 Chinese provinces and autonomous regions,11In this paper, we just study the economy structure in Chinese mainland, and Taiwan, Hong Kong and Macao are not involved. In the following parts, we use ‘region’ to represent a province or autonomous region. The relevant economic data contains the annual GDP of nine economic sectors, and are downloaded from, and we introduce the 31 regions in Appendix A simply. then build several spatial economic networks through the threshold method and the Minimal Spanning Tree method. After the analysis on the structure of the networks and the influence of geographic distance, some conclusions are drawn. First, connectivity distribution of a spatial economic network does not follow the power law. Second, according to the network structure, nine economic sectors could be divided into two groups, and there is significant discrepancy of network structure between these two groups. Moreover, the influence of the geographic distance plays an important role on the structure of a spatial economic network, network parameters are changed with the influence of the geographic distance. At last, 2000 km is the critical value for geographic distance: for real estate and finance, the spearman’s rho with l<2000 is bigger than that with l>2000, and the case is opposite for other economic sectors.

Suggested Citation

  • Hu, Sen & Yang, Hualei & Cai, Boliang & Yang, Chunxia, 2013. "Research on spatial economic structure for different economic sectors from a perspective of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3682-3697.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:17:p:3682-3697
    DOI: 10.1016/j.physa.2013.04.010

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    References listed on IDEAS

    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
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    Cited by:

    1. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
    2. Mary Han & Bill McKelvey, 2016. "How to Grow Successful Social Entrepreneurship Firms? Key Ideas from Complexity Theory," Journal of Enterprising Culture (JEC), World Scientific Publishing Co. Pte. Ltd., vol. 24(03), pages 243-280, September.
    3. Rezvanian, Alireza & Rahmati, Mohammad & Meybodi, Mohammad Reza, 2014. "Sampling from complex networks using distributed learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 224-234.


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