Study on the research evolution of Nobel laureates 2018 based on self-citation network

Abstract

Purpose Science is a continuum of experiences consisting of authors and their publications, and the authors’ experience is an integral part of their work that gets reflected through self-citations. Thus, self-citations can be employed in measuring the relevance between publications and tracking the evolution of research. The paper aims to discuss this issue. Design/methodology/approach Based on the bibliographic data obtained from Scopus, this study constructs and visualizes the self-citation networks of ten Nobel laureates 2018, in the fields of Physiology or Medicine, Physics, Chemistry and Economic Science, to demonstrate the evolving process of each laureate’s research across his or her scholarly career. Findings Statistics indicate that prominent scientists, such as Nobel laureates, have also frequently cited their own publications. However, their self-cited rates are quite low. Self-citations constitute an indispensable part of the citation system but contribute little to authors’ scientific impact, regardless of artificial self-citations. Self-citation networks present a trajectory that shows the evolving process of research across a scientist’s long-term scholarly career. There are obvious differences in self-citation patterns and network structures of different laureates without a disciplinary difference observed. The structures of self-citation networks are significantly influenced by laureates’ productivity. In addition, it is laureates’ own research patterns and citation habits that lead to the diversified patterns and structures of self-citation networks. Research limitations/implications Only scientific achievements presented in the form of publications are investigated and other kinds of scientific output, such as patents, are not included. Moreover, this approach is fit for scientists who have had a longer career and higher productivity. Originality/value This study proves the feasibility and effectiveness of self-citation analysis as a new way to examine research evolution.