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A dynamic co-word network-related approach on the evolution of China’s urbanization research

Author

Listed:
  • Qian-Ru Zhang

    (Tianjin University)

  • Yue Li

    (Tianjin University)

  • Jia-Shu Liu

    (Tianjin University)

  • Yi-Dan Chen

    (Tianjin University)

  • Li-He Chai

    (Tianjin University)

Abstract

In recent years, China’s urbanization has developed very quickly. Many scholars have conducted China’s urbanization research (CUR) and have published a large number of articles. With CUR as a case, we construct the dynamic co-word network to analyze the characteristics of development about the knowledge system (KS). We draw several conclusions from this research. (1) The development of CUR possesses small-world characteristics and scale-free effects. The co-word network of CUR increases significantly to a large-scale network. (2) Betweenness centrality and eigenvector centrality of the dynamic co-word networks positively correlate with node degree. The popular nodes connecting with a large number of topics are also the nodes that occur in the critical paths. The popular keywords in CUR also bridge distant clusters of the related topics. (3) The clustering coefficients indicate that a number of topics with low degrees tend to relate to the adjacent topics more directly to form “conglobation” clusters. The network is a hierarchical clustered structure. The hub keywords play a crucial role in bridging distinct clusters of highly associated keywords and make them form an integrated network. (4) Since 2003, the CUR has begun to develop systematically. From 1998 to 2015, the hotspots in CUR varied diversely, which are highly correlated with social issues and public concerns. (5) We proposed several practical implications on the development of CUR from the dynamic co-word network measures. Beyond the case of CUR’s KS, we hope the versatility of methods in this research also provides enlightenment for other KS studies.

Suggested Citation

  • Qian-Ru Zhang & Yue Li & Jia-Shu Liu & Yi-Dan Chen & Li-He Chai, 2017. "A dynamic co-word network-related approach on the evolution of China’s urbanization research," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1623-1642, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2314-1
    DOI: 10.1007/s11192-017-2314-1
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    References listed on IDEAS

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    2. Seyedmohammadreza Hosseini & Hamed Baziyad & Rasoul Norouzi & Sheida Jabbedari Khiabani & Győző Gidófalvi & Amir Albadvi & Abbas Alimohammadi & Seyedehsan Seyedabrishami, 2021. "Mapping the intellectual structure of GIS-T field (2008–2019): a dynamic co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2667-2688, April.
    3. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    4. Mengyang Wang & Lihe Chai, 2018. "Three new bibliometric indicators/approaches derived from keyword analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 721-750, August.
    5. Xiuwen Chen & Jianping Li & Xiaolei Sun & Dengsheng Wu, 2019. "Early identification of intellectual structure based on co-word analysis from research grants," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 349-369, October.

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