Discoverers in scientific citation data. Issue 2 (May 2019)

• Record Type: Journal Article
• Authors: Shi, Gui-Yuan; Kong, Yi-Xiu; Yuan, Guang-Hui; Wu, Rui-Jie; Zeng, An; Medo, Matúš
• Is Part Of: Journal of informetrics. Volume 13:Issue 2(2019)
• Page Start: 717
• Page End: 725
• Publication Date: 2019-05
• Subjects: Citation networks -- Preferential attachment -- Microscopic mechanisms -- Prediction; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2019.03.017
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: Discoverers often cite highly cited papers short after they are published. We demonstrate the presence of discoverers in citation data. Discoverers can be used to identify future highly cited papers. Discoverers themselves are potential future outstanding researchers. Abstract: Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to the citation network setting, and show that there are authors who frequently cite recent papers that become highly-cited in the future; these authors are referred to as discoverers. We develop a method for early identification of highly-cited papers based on the early citations from discoverers. The results show that the identified discoverers have a consistent citing pattern over time, and the early citations from them can be used as a valuable indicator to predict the future citation counts of a paper. The discoverers themselves are potential future outstanding researchers as they receive more citations than average.