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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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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.

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Acknowledgements

This work was financially supported by the Center for Research in Econometric Theory and Applications (Grant No. 108L900204) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and by the Ministry of Science and Technology (MOST), Taiwan, under Grant Nos. MOST 106-2221-E-011-105-, 108-2221-E-011-053-, 108-3017-F-002-003-.

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Authors and Affiliations

  1. Graduate Institute of Patent, National Taiwan University of Science and Technology, No. 43 Sec. 4 Keelung Rd., Taipei, 10607, Taiwan, ROC

    Chung-Huei Kuan

  2. Center for Research in Econometric Theory and Applications, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan, ROC

    Chung-Huei Kuan

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  1. Chung-Huei Kuan
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Correspondence to Chung-Huei Kuan.

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Kuan, CH. Regarding weight assignment algorithms of main path analysis and the conversion of arc weights to node weights. Scientometrics 124, 775–782 (2020). https://doi.org/10.1007/s11192-020-03468-8

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  • DOI: https://doi.org/10.1007/s11192-020-03468-8

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