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GK index: bridging Gf and K indices for comprehensive author evaluation

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Abstract

The task of accurately predicting scientific impact and ranking the researcher based on impact has emerged as a crucial research challenge, captivating the interest of scholars across diverse domains. This task holds immense importance in enhancing research efficiency, aiding decision-making processes, and facilitating scientific evaluations. For this, the scientific community has put forth a wide array of parameters to identify the most influential researchers. These include citation count, total publication count, hybrid methodologies, the h-index, and also its extended or modified versions. But still, there is a lack of consensus on a single optimal parameter for identifying the most influential author. In this study, we introduce a novel index derived from learning hidden patterns within the mathematics field dataset, comprising data from 1050 researchers evenly split between awardees and non-awardees. Initially, we ranked selected parameters by assessing their values for individual researchers, identifying the top five parameters that most frequently placed awardees within the top 100 records. Additionally, we employed deep learning techniques to identify the top five influential parameters from the initially selected set. Subsequently, we evaluated the disjointness between the results produced by these parameters. To further refine our analysis, we assessed seven different statistical models for combining the top disjoint pair to retain the maximum properties of both parameters. The study’s findings revealed that the gf and k indices exhibited a 0.96 percent disjointness ratio, establishing them as the highest disjoint pair. Moreover, the geometric mean demonstrated a 0.87 percent average impact in retaining the properties of the top disjoint pair, surpassing the other seven models. As a result of this study, we propose a new index obtained by taking the geometric mean of the top disjoint pair which increase the result by 12% as compared to existing best performing individual index performance.

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Notes

  1. https://github.com/ghulammustafacomsat/Mathematics_dataset.

  2. https://github.com/ghulammustafacomsat/dataextraction.

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Acknowledgements

This research is not funded by any organization. Muhammad Tanvir Afzal gave the idea and supervise the research, Ghulam Mustafa did the experiments, interpreted the results and wrote the paper, Abid Rauf review the paper and supervise the research.

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  1. Ghulam Mustafa, Abid Rauf and Muhammad Tanvir Afzal have equally contributed to this work.

Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Taxila, 47080, Pakistan

    Ghulam Mustafa & Abid Rauf

  2. Department of Computing, Shifa Tameer-e-Millat University, Islamabad, 44000, Pakistan

    Muhammad Tanvir Afzal

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Mustafa, G., Rauf, A. & Afzal, M.T. GK index: bridging Gf and K indices for comprehensive author evaluation. Knowl Inf Syst 66, 5203–5238 (2024). https://doi.org/10.1007/s10115-024-02119-1

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