Rainbow ranking: an adaptable, multidimensional ranking method for publication sets

Abstract

Various scientometric indices have been proposed in an attempt to express the quantitative and qualitative characteristics of scientific output. However, fully capturing the performance and impact of a scientific entity (author, journal, institution, conference, etc.) still remains an open research issue, as each proposed index focuses only on particular aspects of scientific performance. Therefore, scientific evaluation can be viewed as a multi-dimensional ranking problem, where dimensions represent the assorted scientometric indices. To address this problem, the skyline operator has been proposed in Sidiropoulos et al. (J Informetr 10(3):789–813, 2016) with multiple combinations of dimensions. In the present work, we introduce a new index derived from the utilization of the skyline operator, called Rainbow Ranking or RR-index that assigns a category score to each scientific entity instead of producing a strict ordering of the ranked entities. Our RR-index allows the combination of any known indices depending on the purposes of the evaluation and outputs a single number metric expressing multi-criteria relative ranking and can be applied to any scientific entity such as authors and journals. The proposed methodology was experimentally evaluated using a dataset of over 105,000 scientists from the Computer Science field. © 2018, Akadémiai Kiadó, Budapest, Hungary.