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Application of Time-Weighted PageRank Method with Citation Intensity for Assessing the Recent Publication Productivity and Partners Selection in R&D Collaboration

Author

Listed:
  • Andrii Biloshchytskyi

    (University Administration Office, Astana IT University, Astana 010000, Kazakhstan
    Department of Information Technology, Kyiv National University of Construction and Architecture, 03037 Kyiv, Ukraine)

  • Oleksandr Kuchanskyi

    (Department of Computational and Data Science, Astana IT University, Astana 010000, Kazakhstan
    Department of Information Control Systems and Technologies, Uzhhorod National University, 88000 Uzhhorod, Ukraine
    Department of Biomedical Cybernetics, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine)

  • Aidos Mukhatayev

    (Department of Social Disciplines, Astana IT University, Astana 010000, Kazakhstan
    National Higher Education Development Center, Ministry of Science and Higher Education, Astana 010000, Kazakhstan)

  • Yurii Andrashko

    (Department of System Analysis and Optimization Theory, Uzhhorod National University, 88000 Uzhhorod, Ukraine)

  • Sapar Toxanov

    (Center of Competence and Excellence, Astana IT University, Astana 010000, Kazakhstan)

  • Adil Faizullin

    (University Administration Office, Astana IT University, Astana 010000, Kazakhstan)

  • Khanat Kassenov

    (Quality Assurance Department, Astana IT University, Astana 010000, Kazakhstan)

Abstract

This article considers the problem of assessing the recent publication productivity of scientists based on PageRank class methods and proposes to use these assessments to solve the problem of selecting scientific partners for R&D projects. The methods of PageRank, Time-Weighted PageRank, and the Time-Weighted PageRank method with Citation Intensity (TWPR-CI) were used as a basis for calculating the publication productivity of individual subjects or scientists. For verification, we used the Citation Network Dataset (Ver. 14) of more than 5 million STEM publications with 36 million citations. The dataset is based on data from ACM, DBLP, and Microsoft Academic Graph databases. Only those individual subjects who published at least two articles after 2000, with at least one of these articles cited at least once before 2023 year, were analyzed. Thus, the number of individual subjects was reduced to 1,042,122, and the number of scientific publications was reduced to 2,422,326. For each of the methods, a range of estimates of productivity is indicated, which are obtained as a result and possible options for making decisions on the selection of potential individual subjects as performers of R&D projects. One of the key advantages of the TWPR-CI method is that it gives priority to those researchers who have recently published and been cited frequently in their respective research areas. This ensures that the best potential R&D project executors are selected, which should minimize the impact of subjective factors on this choice. We believe that the proposed concept for selecting potential R&D project partners could help to reduce the risks associated with these projects and facilitate the involvement of the most suitable specialists in the relevant area of knowledge.

Suggested Citation

  • Andrii Biloshchytskyi & Oleksandr Kuchanskyi & Aidos Mukhatayev & Yurii Andrashko & Sapar Toxanov & Adil Faizullin & Khanat Kassenov, 2024. "Application of Time-Weighted PageRank Method with Citation Intensity for Assessing the Recent Publication Productivity and Partners Selection in R&D Collaboration," Publications, MDPI, vol. 12(4), pages 1-21, December.
    • Handle: RePEc:gam:jpubli:v:12:y:2024:i:4:p:48-:d:1542921
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    References listed on IDEAS

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    1. Paul Benneworth & Julia Olmos-Peñuela, 2024. "An openness framework for ex ante evaluation of societal impact of research," Research Evaluation, Oxford University Press, vol. 33, pages 150-157.
    2. Gattringer, Regina & Wiener, Melanie & Strehl, Franz, 2017. "The challenge of partner selection in collaborative foresight projects," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 298-310.
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