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Regarding weight assignment algorithms of main path analysis and the conversion of arc weights to node weights

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  • Chung-Huei Kuan

    (National Taiwan University of Science and Technology
    National Taiwan University)

Abstract

In a recent article, Liu et al. (Scientometrics 119(1):379–391, 2019) elaborated a number of issues of the main path analysis (MPA) and provided valuable insight into its application. Among these issues, the authors compared three weight assignment algorithms and suggested that one is preferable in simulating the knowledge diffusion scenario. The authors further stated that one may convert a document’s related arc weights assigned by these algorithms into a weight of the document itself by taking the average of its incident and outgoing arc weights, and claimed that a document highly weighted as such may be considered as having a great impact. In this Letter, we address these two issues: (1) choice of weight assignment algorithms, and (2) conversion of arc weights to node weights from a different perspective and provide alternative suggestions, in the hope that we may enrich the discussion for MPA.

Suggested Citation

  • Chung-Huei Kuan, 2020. "Regarding weight assignment algorithms of main path analysis and the conversion of arc weights to node weights," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 775-782, July.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03468-8
    DOI: 10.1007/s11192-020-03468-8
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    References listed on IDEAS

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    1. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
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    Cited by:

    1. Xiaorui Jiang & Junjun Liu, 2023. "Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 546-569, May.
    2. Kim, Erin H.J. & Jeong, Yoo Kyung & Kim, YongHwan & Song, Min, 2022. "Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).

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