$C^3$-index: Revisiting Authors' Performance Measure

TL;DR

The paper introduces the $C^3$-index, a new author performance metric that leverages citations and collaboration data through a multi-layered network and a modified PageRank algorithm, effectively resolving ties and predicting future success.

Contribution

It proposes a novel $C^3$-index based on a weighted multi-layered network and a modified PageRank algorithm, addressing limitations of existing indices like h-index.

Findings

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

It effectively breaks ties among low-ranked authors.

It can predict future achievers early in their careers.

Abstract

Author performance indices (such as h-index and its variants) fail to resolve ties while ranking authors with low index values (majority in number) which includes the young researchers. In this work we leverage the citations as well as collaboration profile of an author in a novel way using a weighted multi-layered network and propose a variant of page-rank algorithm to obtain a new author performance measure, $C^3$-index. Experiments on a massive publication dataset reveal several interesting characteristics of our metric: (i) we observe that $C^3$-index is consistent over time, (ii) $C^3$-index has high potential to break ties among low rank authors, (iii) $C^3$-index can effectively be used to predict future achievers at the early stage of their career.