CITEX: A new citation index to measure the relative importance of authors and papers in scientific publications

TL;DR

CITEX is a novel citation index that simultaneously assigns normalized importance scores to authors and papers, providing a more accurate and objective measure of scientific impact through an iterative graph-based computation.

Contribution

This paper introduces CITEX, the first citation index to evaluate both authors and papers simultaneously using a convergent iterative algorithm on a publication graph.

Findings

CITEX scores closely match intuitive assessments of impact.

The algorithm converges reliably and runs efficiently.

CITEX outperforms traditional citation metrics in accuracy.

Abstract

Evaluating the performance of researchers and measuring the impact of papers written by scientists is the main objective of citation analysis. Various indices and metrics have been proposed for this. In this paper, we propose a new citation index CITEX, which gives normalized scores to authors and papers to determine their rankings. To the best of our knowledge, this is the first citation index which simultaneously assigns scores to both authors and papers. Using these scores, we can get an objective measure of the reputation of an author and the impact of a paper. We model this problem as an iterative computation on a publication graph, whose vertices are authors and papers, and whose edges indicate which author has written which paper. We prove that this iterative computation converges in the limit, by using a powerful theorem from linear algebra. We run this algorithm on several examples, and find that the author and paper scores match closely with what is suggested by our intuition. The algorithm is theoretically sound and runs very fast in practice. We compare this index with several existing metrics and find that CITEX gives far more accurate scores compared to the traditional metrics.