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COLLNET Journal of Scientometrics and Information Management

(Since 2007)
Open Access ·Peer-reviewed·ISSN (Online): 2168-930X·ISSN (Print): 0973-7766

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Half-Yearly Journal: Publishes research and articles on scientometrics and information management, including bibliometric analysis and quality assurance models.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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COLLNET Journal of Scientometrics and Information Management
Open Access Research Article

Formulating rules to identify key researchers in computer science : A quantitative approach

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pp. 91–122Vol. 19Issue 1June 2025DOI: 10.47974/CJSIM-2024-11007XML
Published Online:
01 Jul 2025
Article type:
Research Article
Language:
EN
Article no.:
CJSIM-2024-11007
Pages:
91–122

Abstract

In the vast expanse of scientific literature, determining which researchers have made the most significant contributions to their field is essential. However, the scientific community lacks a standardized set of criteria for identifying impactful researchers. Existing platforms, like Google Scholar, Semantic Scholar & Web of Science, provide useful metrics like total publications, citation counts, and h-index, but no universally accepted framework exists to comprehensively evaluate a researcher’s true impact. This study proposes a novel framework for identifying impactful researchers within the computer science domain. The framework integrates 64 distinct quantitative parameters across categories such as citation-based, publication-based, author-count-based, and age-weighted metrics to evaluate researchers’ contributions. Unlike traditional methods that focus on individual indicators, our framework provides a comprehensive evaluation model that considers multiple factors simultaneously. The parameters were selected based on their importance scores, determined by their ability to classify awardees and non-awardees. Experiments were performed on a balanced dataset comprising 600 awardees and 600 non-awardees, yielding classification accuracies ranging from 57% to 78% in recognizing influential researchers. The top-ranked features from each parameter category effectively promoted 50% to 55% of awardees into the top 100 researcher rankings. These findings offer a robust tool for academic institutions and organizations seeking to identify impactful researchers. The proposed framework enables more objective evaluation and provides actionable insights for individual researchers striving for recognition and influence in their field.

Keywords

Researcher evaluation metricsParameter prioritizationMulti-layer perceptron (MLP)H-index variantsComputer science domain datasets

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