IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v377y2007i1p302-314.html
   My bibliography  Save this article

Weighted network properties of Chinese nature science basic research

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437106012064
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2006.11.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:377:y:2007:i:1:p:302-314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.