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Visual topical analysis of Chinese and American Library and Information Science research institutions

Author

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  • An, Lu
  • Yu, Chuanming
  • Li, Gang

Abstract

Research institutions play an important role in scientific research and technical innovation. The topical analysis of research institutions in different countries can facilitate mutual learning and promote potential collaboration. In this study, we illustrate how an unsupervised artificial neural network technique Self-Organizing Map (SOM) can be used to visually analyze the research fields of research institutions. A novel SOM display named Compound Component Plane (CCP) was presented and applied to determine the institutions which made significant contributions to the salient research fields. Eighty-seven Chinese and American LIS institutions and the technical LIS fields were taken as examples. Potential international and domestic collaborators were identified based upon their research similarities. An approach of dividing research institutions into clusters was proposed based on their geometric distances in the SOM display, the U-matrix values and the most salient research topics they involved. The concepts of swarm institutions, pivots and landmarks were also defined and their instances were identified.

Suggested Citation

  • An, Lu & Yu, Chuanming & Li, Gang, 2014. "Visual topical analysis of Chinese and American Library and Information Science research institutions," Journal of Informetrics, Elsevier, vol. 8(1), pages 217-233.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:217-233
    DOI: 10.1016/j.joi.2013.12.002
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    References listed on IDEAS

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    1. Kevin W. Boyack, 2009. "Using detailed maps of science to identify potential collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 27-44, April.
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    Cited by:

    1. Lu An & Xia Lin & Chuanming Yu & Xinwen Zhang, 2015. "Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1605-1634, December.
    2. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    3. Guo Chen & Lu Xiao & Chang-ping Hu & Xue-qin Zhao, 2015. "Identifying the research focus of Library and Information Science institutions in China with institution-specific keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 707-724, May.

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