Search for evergreens in science : A functional data analysis

Zhang, Ruizhi and Wang, Jian and Mei, Yajun (2017) Search for evergreens in science : A functional data analysis. Journal of Informetrics, 11 (3). pp. 629-644. ISSN 1751-1577

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Abstract

Evergreens in science are papers that display a continual rise in annual citations without decline, at least within a sufficiently long time period. Aiming to better understand evergreens in particular and patterns of citation trajectory in general, this paper develops a functional data analysis method to cluster citation trajectories of a sample of 1699 research papers published in 1980 in the American Physical Society (APS) journals. We propose a functional Poisson regression model for individual papers’ citation trajectories, and fit the model to the observed 30-year citations of individual papers by functional principal component analysis and maximum likelihood estimation. Based on the estimated paper-specific coefficients, we apply the K-means clustering algorithm to cluster papers into different groups, for uncovering general types of citation trajectories. The result demonstrates the existence of an evergreen cluster of papers that do not exhibit any decline in annual citations over 30 years.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Informetrics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
?? citation trajectoryevergreenfunctional poisson regressionfunctional principal component analysisk-means clusteringcomputer science applicationslibrary and information sciencesmodelling and simulationmanagement science and operations researchapplied mathem ??
ID Code:
209266
Deposited By:
Deposited On:
06 Nov 2023 11:10
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2024 00:30