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Ranking scientific authors is an important but challenging task, mostly dueto the dynamic nature of the evolving scientific publications. The basicindicators of an author's productivity and impact are still the number ofpublications and the citation count (leading to the popular metrics such ash-index, g-index etc.). H-index and its popular variants are mostly effectivein ranking highly-cited authors, thus fail to resolve ties while rankingmedium-cited and low-cited authors who are majority in number. Therefore, thesemetrics are inefficient to predict the ability of promising young researchersat the beginning of their career. In this paper, we propose $C^3$-index thatcombines the effect of citations and collaborations of an author in asystematic way using a weighted multi-layered network to rank authors. Weconduct our experiments on a massive publication dataset of Computer Scienceand show that - (i) $C^3$-index is consistent over time, which is one of thefundamental characteristics of a ranking metric, (ii) $C^3$-index is asefficient 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 theranking of medium-cited and low-cited authors, (iv) $C^3$-index can also beused to predict future achievers at the early stage of their career.

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