Citation network centrality: a scientific awards predictor?

TL;DR

This paper investigates the $K$-index, a centrality measure in citation networks, and demonstrates its potential to predict major scientific awards by identifying influential researchers.

Contribution

It introduces the use of the $K$-index as a predictor for scientific prizes and provides an experimental shortlist of candidates, including a recent Nobel laureate.

Findings

The $K$-index correlates better with Nobel prizes than other indices.

A shortlist of 12 candidates includes the 2019 Nobel laureate.

The $K$-index can be updated annually for prize prediction.

Abstract

The $K$-index is an easily computable centrality index in complex networks, such as a scientific citations network. A researcher has a $K$-index equal to $K$ if he or she is cited by $K$ articles that have at least $K$ citations. The $K$-index has several advantages over Hirsh's $h$-index and, in previous studies, has shown better correlation with Nobel prizes than any other index given by the {\em Web of Science}, including the $h$-index. It is plausible that researchers who are the most connected to other scientifically well-connected researchers are the most likely to be doing important work and more likely to be awarded major prizes in a given area. However, the correlation found does not imply causation. Here we perform an experiment using the $K$-index, producing a shortlist of twelve candidates for major scientific prizes, including the Physics Nobel award, in the near future. For example, our top-12 $K$-index list naturally selects the 2019 Nobel laureate, James Peebles. The list can be updated annually and should be compared to laureates of the following years