How to Measure the Researcher Impact with the Aid of its Impactable Area: A Concrete Approach Using Distance Geometry

Beniamino Cappelletti-Montano (), Gianmarco Cherchi (), Benedetto Manca (), Stefano Montaldo () and Monica Musio ()
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Beniamino Cappelletti-Montano: University of Cagliari
Gianmarco Cherchi: University of Cagliari
Benedetto Manca: University of Cagliari
Stefano Montaldo: University of Cagliari
Monica Musio: University of Cagliari

Journal of Classification, 2025, vol. 42, issue 1, No 13, 253-281

Abstract: Abstract Assuming that the subject of each scientific publication can be identified by one or more classification entities, we address the problem of determining a similarity function (distance) between classification entities based on how often two classification entities are used in the same publication. This similarity function is then used to obtain a representation of the classification entities as points of an Euclidean space of a suitable dimension by means of optimization and dimensionality reduction algorithms. This procedure allows us also to represent the researchers as points in the same Euclidean space and to determine the distance between researchers according to their scientific production. As a case study, we consider as classification entities the codes of the American Mathematical Society Classification System.

Keywords: Bibliometrics; Dimensionality reduction; Linear programming (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-024-09490-2

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