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A nonlinear collective credit allocation in scientific publications

Author

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  • Fenghua Wang

    (Beijing Normal University)

  • Ying Fan

    (Beijing Normal University)

  • An Zeng

    (Beijing Normal University)

  • Zengru Di

    (Beijing Normal University)

Abstract

Collaboration among researchers plays an important role in scientific discoveries, especially in multidisciplinary research. How to allocate credit reasonably to coauthors of a paper is a long-standing problem in the science of sciences. The collective credit allocation method (CCA method) proposed by Shen, H. W. and Barabási, A. L. provides a novel view to solve this problem, which measures the coauthors’ contribution to a paper based on the citation process by the scientific community. Nevertheless, the existing collective allocation method assigns equal weights to citing papers, which is sensitive to the malicious manipulation. In this paper, we propose a nonlinear collective credit allocation method (NCCA method) that assigns different strength to citing papers according to papers’ scientific impact when measuring papers’ similarity. Compared to the CCA method, we find that the NCCA method assigns more credits to Nobel laureates in the Nobel-winning papers. Moreover, the NCCA method is robust against random perturbations and the malicious manipulation in both Nobel-prize papers and ordinary papers. Furthermore, the collective credit allocation method can also modify h index.

Suggested Citation

  • Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "A nonlinear collective credit allocation in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1655-1668, June.
    • Handle: RePEc:spr:scient:v:119:y:2019:i:3:d:10.1007_s11192-019-03107-x
    DOI: 10.1007/s11192-019-03107-x
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    References listed on IDEAS

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    2. Xing, Yanmeng & Wang, Fenghua & Zeng, An & Ying, Fan, 2021. "Solving the cold-start problem in scientific credit allocation," Journal of Informetrics, Elsevier, vol. 15(3).

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