Title: Search for Evergreens in Science: A Functional Data Analysis,
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.