Podcast on "Demystifying Algorithmic Complexities and Geometric Review of the h-Index"

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This podcast discusses the paper "Demystifying algorithmic complexities and geometric review of the ‘h’-index" written by Kaushik Ghosh and Mayukh Mukhopadhyay that aims to clarify the calculation and implications of the h-index, a metric used to measure a researcher's productivity and impact. It provides a detailed breakdown of the h-index's definition, its advantages, and its limitations. The authors then present a simple algorithmic approach to calculate the h-index using Python code. They further delve into a geometric analysis, exploring how the h-index can be visualised and determined using Cartesian geometry. The paper concludes with postulates for geometrically determining the h-index based on the authors' case studies.

Connected to paperThis paper is a preprint and has not been certified by peer review

Demystifying Algorithmic Complexities and Geometric Review of the 'h'-Index

Authors

Kaushik Ghosh, Mayukh Mukhopadhyay

Abstract

The current discourse delves into the effectiveness of h-index as an author level metric. It further reviews and explains the algorithmic complexity of calculating h-index through algebraic method. To conduct the algebraic analysis propositional algebra, algorithm and coding techniques have been used. Some use cases have been identified with a finite-data-set/set-of-array to demonstrate the coding techniques and for further analysis. Finally, the explanation and calculative complexities to determine the index have been further simplified through geometric method of calculating the h-index using the similar use cases that was used for coding. It is concluded that determination of the h-index using Euclidean geometric method with Cartesian frame of reference provides a through and visual clarification. Finally, a set of postulates has been proposed at the end of the paper, based on the case studies.

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