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The Pagerank-Index: Going beyond Citation Counts in Quantifying Scientific Impact of Researchers

Author

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  • Upul Senanayake
  • Mahendra Piraveenan
  • Albert Zomaya

Abstract

Quantifying and comparing the scientific output of researchers has become critical for governments, funding agencies and universities. Comparison by reputation and direct assessment of contributions to the field is no longer possible, as the number of scientists increases and traditional definitions about scientific fields become blurred. The h-index is often used for comparing scientists, but has several well-documented shortcomings. In this paper, we introduce a new index for measuring and comparing the publication records of scientists: the pagerank-index (symbolised as π). The index uses a version of pagerank algorithm and the citation networks of papers in its computation, and is fundamentally different from the existing variants of h-index because it considers not only the number of citations but also the actual impact of each citation. We adapt two approaches to demonstrate the utility of the new index. Firstly, we use a simulation model of a community of authors, whereby we create various ‘groups’ of authors which are different from each other in inherent publication habits, to show that the pagerank-index is fairer than the existing indices in three distinct scenarios: (i) when authors try to ‘massage’ their index by publishing papers in low-quality outlets primarily to self-cite other papers (ii) when authors collaborate in large groups in order to obtain more authorships (iii) when authors spend most of their time in producing genuine but low quality publications that would massage their index. Secondly, we undertake two real world case studies: (i) the evolving author community of quantum game theory, as defined by Google Scholar (ii) a snapshot of the high energy physics (HEP) theory research community in arXiv. In both case studies, we find that the list of top authors vary very significantly when h-index and pagerank-index are used for comparison. We show that in both cases, authors who have collaborated in large groups and/or published less impactful papers tend to be comparatively favoured by the h-index, whereas the pagerank-index highlights authors who have made a relatively small number of definitive contributions, or written papers which served to highlight the link between diverse disciplines, or typically worked in smaller groups. Thus, we argue that the pagerank-index is an inherently fairer and more nuanced metric to quantify the publication records of scientists compared to existing measures.

Suggested Citation

  • Upul Senanayake & Mahendra Piraveenan & Albert Zomaya, 2015. "The Pagerank-Index: Going beyond Citation Counts in Quantifying Scientific Impact of Researchers," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-34, August.
  • Handle: RePEc:plo:pone00:0134794
    DOI: 10.1371/journal.pone.0134794
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    References listed on IDEAS

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    Cited by:

    1. Shubham Sharma & Usha Lenka, 2022. "On the shoulders of giants: uncovering key themes of organizational unlearning research in mainstream management journals," Review of Managerial Science, Springer, vol. 16(6), pages 1599-1695, August.
    2. Nisar Ali & Zahid Halim & Syed Fawad Hussain, 2023. "An artificial intelligence-based framework for data-driven categorization of computer scientists: a case study of world’s Top 10 computing departments," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1513-1545, March.
    3. Dinesh Pradhan & Partha Sarathi Paul & Umesh Maheswari & Subrata Nandi & Tanmoy Chakraborty, 2017. "$$C^3$$ C 3 -index: a PageRank based multi-faceted metric for authors’ performance measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 253-273, January.
    4. Massucci, Francesco Alessandro & Docampo, Domingo, 2019. "Measuring the academic reputation through citation networks via PageRank," Journal of Informetrics, Elsevier, vol. 13(1), pages 185-201.
    5. Fabio Zagonari, 2019. "Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined," Publications, MDPI, vol. 7(2), pages 1-30, May.
    6. Jun Zhang & Zhaolong Ning & Xiaomei Bai & Xiangjie Kong & Jinmeng Zhou & Feng Xia, 2017. "Exploring time factors in measuring the scientific impact of scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1301-1321, September.
    7. Zhi Li & Qinke Peng & Che Liu, 2016. "Two citation-based indicators to measure latent referential value of papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1299-1313, September.
    8. Hao Wang & Hua-Wei Shen & Xue-Qi Cheng, 2016. "Scientific credit diffusion: Researcher level or paper level?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 827-837, November.

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