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A new approach for main path analysis: Decay in knowledge diffusion

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  • John S. Liu
  • Chung-Huei Kuan

Abstract

type="main"> Main path analysis is a powerful tool for extracting the backbones of a directed network and has been applied widely in bibliometric studies. In contrast to the no-decay assumption in the traditional approach, this study proposes a novel technique by assuming that the strength of knowledge decays when knowledge contained in one document is passed on to another document down the citation chain. We propose three decay models, arithmetic decay, geometric decay, and harmonic decay, along with their theoretical properties. In general, results of the proposed decay models depend largely on the local structure of a citation network as opposed to the global structure in the traditional approach. Thus, the significance of citation links and the associated documents that are overemphasized by the global structure in the traditional no-decay approach is treated more properly. For example, the traditional approach commonly assigns high value to documents that heavily reference others, such as review articles. Specifically in the geometric and harmonic decay models, only truly significant review articles will be included in the resulting main paths. We demonstrate this new approach and its properties through the DNA literature citation network.

Suggested Citation

  • John S. Liu & Chung-Huei Kuan, 2016. "A new approach for main path analysis: Decay in knowledge diffusion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(2), pages 465-476, February.
  • Handle: RePEc:bla:jinfst:v:67:y:2016:i:2:p:465-476
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    File URL: http://hdl.handle.net/10.1002/asi.23384
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    Cited by:

    1. Tang-Min Hsieh & Kai-Ying Chen, 2024. "Developmental Trajectories of Electric Vehicle Research in a Circular Economy: Main Path Analysis," Sustainability, MDPI, vol. 16(18), pages 1-40, September.
    2. Kox, Henk L.M., 2022. "Testing the knowledge-capital model of foreign direct investment: New evidence," MPRA Paper 114177, University Library of Munich, Germany.
    3. Kox, Henk L.M., 2022. "Revisiting the knowledge-capital model of foreign direct investment: New multi-country evidence," MPRA Paper 114559, University Library of Munich, Germany, revised 14 Sep 2022.
    4. Ichiro Watanabe & Soichiro Takagi, 2022. "NK model-based analysis of technological trajectories: a study on the technological field of computer graphic processing systems," Evolutionary and Institutional Economics Review, Springer, vol. 19(1), pages 119-140, April.
    5. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    6. Kox, Henk L.M., 2023. "Testing an extended knowledge-capital model of foreign direct investment," MPRA Paper 117266, University Library of Munich, Germany.
    7. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    9. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    10. Kox, Henk L.M., 2022. "Linking the knowledge-capital model of foreign direct investment with national knowledge systems," EconStor Preprints 266495, ZBW - Leibniz Information Centre for Economics.
    11. Zenghui Yue & Haiyun Xu & Guoting Yuan & Yan Qi, 2022. "Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7593-7613, December.
    12. 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).
    13. 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.
    14. Ichiro Watanabe & Soichiro Takagi, 2021. "Technological Trajectory Analysis of Patent Citation Networks: Examining the Technological Evolution of Computer Graphic Processing Systems," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 1-25, June.
    15. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    16. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    17. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    18. Clemens Blümel & Alexander Schniedermann, 2020. "Studying review articles in scientometrics and beyond: a research agenda," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 711-728, July.
    19. Mei Hsiu-Ching Ho & John S. Liu & Kerr C.-T. Chang, 2017. "To include or not: the role of review papers in citation-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 65-76, January.

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