Quantifying the influence of scientists and their publications: Distinguish prestige from popularity

TL;DR

This paper introduces a parameter-free interactive model that evaluates scientific influence by considering the prestige of citing scientists, effectively distinguishing true influence from mere citation popularity.

Contribution

It proposes a novel, iterative algorithm on author-paper networks that accounts for prestige, improving upon simple citation counts for ranking scientific influence.

Findings

The method effectively differentiates prestige from popularity.

Experiments on econophysics publications validate the model's effectiveness.

The approach provides more accurate influence rankings than traditional citation metrics.

Abstract

The number of citations is a widely used metric to evaluate the scientific credit of papers, scientists and journals. However, it does happen that a paper with fewer citations from prestigious scientists is of higher influence than papers with more citations. In this paper, we argue that from whom the paper is being cited is of higher significance than merely the number of received citations. Accordingly, we propose an interactive model on author-paper bipartite networks as well as an iterative algorithm to get better rankings for scientists and their publications. The main advantage of this method is twofold: (i) it is a parameter-free algorithm; (ii) it considers the relationship between the prestige of scientists and the quality of their publications. We conducted real experiments on publications in econophysics, and applied this method to evaluate the influences of related scientific journals. The comparisons between the rankings by our method and simple citation counts suggest that our method is effective to distinguish prestige from popularity.