$C^3$-index: A PageRank based multi-faceted metric for authors' performance measurement

TL;DR

The paper introduces the $C^3$-index, a new multi-faceted metric based on PageRank, designed to improve author ranking by incorporating citations and collaborations, especially aiding in ranking less-cited researchers and predicting future achievers.

Contribution

It proposes the $C^3$-index, a novel PageRank-based metric that systematically combines citations and collaborations to enhance author performance measurement.

Findings

$C^3$-index is consistent over time.

It effectively ranks highly-cited authors.

It predicts future successful researchers.

Abstract

Ranking scientific authors is an important but challenging task, mostly due to the dynamic nature of the evolving scientific publications. The basic indicators of an author's productivity and impact are still the number of publications and the citation count (leading to the popular metrics such as h-index, g-index etc.). H-index and its popular variants are mostly effective in ranking highly-cited authors, thus fail to resolve ties while ranking medium-cited and low-cited authors who are majority in number. Therefore, these metrics are inefficient to predict the ability of promising young researchers at the beginning of their career. In this paper, we propose $C^3$-index that combines the effect of citations and collaborations of an author in a systematic way using a weighted multi-layered network to rank authors. We conduct our experiments on a massive publication dataset of Computer Science and show that - (i) $C^3$-index is consistent over time, which is one of the fundamental characteristics of a ranking metric, (ii) $C^3$-index is as efficient as h-index and its variants to rank highly-cited authors, (iii) $C^3$-index can act as a conflict resolution metric to break ties in the ranking of medium-cited and low-cited authors, (iv) $C^3$-index can also be used to predict future achievers at the early stage of their career.