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Search for evergreens in science: A functional data analysis
Journal of Informetrics (2017)
  • Ruizhi Zhang, Georgia Institute of Technology - Main Campus
  • Jian Wang, KU Leuven
  • Yajun Mei, Georgia Institute of Technology - Main Campus
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.
  • citation trajectory,
  • evergreen,
  • functional Poisson regression,
  • functional principal component analysis,
  • K-means clustering
Publication Date
May 17, 2017
Citation Information
Ruizhi Zhang, Jian Wang and Yajun Mei. "Search for evergreens in science: A functional data analysis" Journal of Informetrics Vol. 11 Iss. 3 (2017) p. 629 - 644 ISSN: 1751-1577
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