Solving the cold-start problem in scientific credit allocation

Xing, Yanmeng; Wang, Fenghua; Zeng, An; Fan, Ying

doi:10.1016/j.joi.2021.101157

Cited by 3 publications

(3 citation statements)

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“…The original method and other variants (Bao et al, 2017;F. H. Wang et al, 2019; J. P. Xing et al, 2021) are tested in empirical data and demonstrate high accuracy in determining Nobel Prize laureates from the team that uncovers the scientific discover together.…”

Section: Research Papermentioning

confidence: 99%

“…The final credits of the co-authors are determined by the product of the author involvement matrix A, residual influence Φ, and the strength vector s. J. P. propose IDCA method that uses the same residual influence Φ as that in DCA but modifies the strength vector s. The element value of s is calculated by summing over the product of the PageRank value and the citation number of the citing paper. Xing et al (2021) propose the CoCA method that modifies the collection of papers in matrix A and the value of the strength vector s. CoCA focuses on the subsequent works by co-authors of the target paper (papers authored by at least one of the co-authors). These subsequent papers generate the involvement matrix A.…”

Section: Journal Of Data and Information Sciencementioning

confidence: 99%

“…H. Wang et al, 2019;J. P. Wang et al, 2019;Shen et al, 2014;Xing et al, 2021) consider papers receiving the Nobel Physics Prize as the validation set to verify the accuracy of the algorithms. Indeed, the choice of Nobel Prize winners reflects the scientific community's recognition: the author who makes the most contribution to the Nobel Prize-winning papers is considered as the rightful owner of the scientific achievement(Lo, 2013).Because the Nobel Physics Prize can be awarded to a maximum of three authors and two different scientific discoveries each year, there are cases when multiple authors of a Nobel Prize-winning paper receive the award.…”

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An author credit allocation method with improved distinguishability and robustness

Jia

2023

Journal of Data and Information Science

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Purpose The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. Design/methodology/approach We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Finding Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Research limitations Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. Practical implications NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Originality/value Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.

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Section: Research Papermentioning

confidence: 99%

Section: Journal Of Data and Information Sciencementioning

confidence: 99%

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