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Weighted network properties of Chinese nature science basic research

Author

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  • Liu, Jian-Guo
  • Xuan, Zhao-Guo
  • Dang, Yan-Zhong
  • Guo, Qiang
  • Wang, Zhong-Tuo

Abstract

Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas (WNRA). In WNRA, two research areas, which is represented by the subject codes, are considered to be connected if they have been filled in one or more requisition papers. The edge weight is defined as the number of requisition papers which have been filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have been filled in this code, including the papers which have been filled in it alone. Here we study a variety of nonlocal statistics for WNRA, such as typical distance between research areas and measure of centrality such as betweenness. These statistical characteristics can illuminate the global development trend of Chinese scientific study. It is also helpful to adjust the code system to reflect the real status more accurately. Finally, we present a plausible model for the formation and structure of WNRA with the observed properties.

Suggested Citation

  • Liu, Jian-Guo & Xuan, Zhao-Guo & Dang, Yan-Zhong & Guo, Qiang & Wang, Zhong-Tuo, 2007. "Weighted network properties of Chinese nature science basic research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 302-314.
    • Handle: RePEc:eee:phsmap:v:377:y:2007:i:1:p:302-314
    DOI: 10.1016/j.physa.2006.11.011
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    Citations

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    Cited by:

    1. Liu, Jin-Hu & Wang, Jun & Shao, Junming & Zhou, Tao, 2016. "Online social activity reflects economic status," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 581-589.
    2. Yang, Guang-Yong & Hu, Zhao-Long & Liu, Jian-Guo, 2015. "Knowledge diffusion in the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 429-436.
    3. Zivar Sabaghinejad & Farideh Osareh & Fatima Baji & Parastou Parsaei Mohammadi, 2016. "Estimating the partnership ability of Scientometrics journal authors based on WoS from 2001 to 2013 according to ϕ-index1," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 73-84, October.
    4. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    5. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    6. Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    7. Wang, Junjie & Zhou, Shuigeng & Guan, Jihong, 2011. "Characteristics of real futures trading networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 398-409.
    8. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
    9. Tehmina Amjad & Ying Ding & Ali Daud & Jian Xu & Vincent Malic, 2015. "Topic-based heterogeneous rank," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 313-334, July.
    10. Yi Bu & Tian-yi Liu & Win-bin Huang, 2016. "MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 143-166, July.

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