# Search for Evergreens in Science: A Functional Data Analysis

## TL;DR

This paper introduces a functional data analysis approach to identify and classify citation trajectory patterns of scientific papers, revealing a distinct evergreen cluster with sustained citations over 30 years.

## Contribution

It develops a novel functional Poisson regression model and clustering method to analyze long-term citation patterns, specifically identifying evergreen papers.

## Key findings

- Existence of a distinct evergreen citation cluster
- Method successfully classifies papers into different citation trajectory groups
- Provides insights into long-term scientific impact patterns

## 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.