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Search for evergreens in science: A functional data analysis

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Search for evergreens in science: A functional data analysis. / Zhang, Ruizhi; Wang, Jian; Mei, Yajun.
In: Journal of Informetrics, Vol. 11, No. 3, 31.08.2017, p. 629-644.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Zhang, R, Wang, J & Mei, Y 2017, 'Search for evergreens in science: A functional data analysis', Journal of Informetrics, vol. 11, no. 3, pp. 629-644. https://doi.org/10.1016/j.joi.2017.05.007

APA

Zhang, R., Wang, J., & Mei, Y. (2017). Search for evergreens in science: A functional data analysis. Journal of Informetrics, 11(3), 629-644. https://doi.org/10.1016/j.joi.2017.05.007

Vancouver

Zhang R, Wang J, Mei Y. Search for evergreens in science: A functional data analysis. Journal of Informetrics. 2017 Aug 31;11(3):629-644. doi: 10.1016/j.joi.2017.05.007

Author

Zhang, Ruizhi ; Wang, Jian ; Mei, Yajun. / Search for evergreens in science : A functional data analysis. In: Journal of Informetrics. 2017 ; Vol. 11, No. 3. pp. 629-644.

Bibtex

@article{a4a782be53ff45c4b1f2ca5a629abbf4,
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{\textquoteright} 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.",
keywords = "Citation trajectory, Evergreen, Functional Poisson regression, Functional principal component analysis, K-means clustering",
author = "Ruizhi Zhang and Jian Wang and Yajun Mei",
year = "2017",
month = aug,
day = "31",
doi = "10.1016/j.joi.2017.05.007",
language = "English",
volume = "11",
pages = "629--644",
journal = "Journal of Informetrics",
issn = "1751-1577",
publisher = "Elsevier BV",
number = "3",

}

RIS

TY - JOUR

T1 - Search for evergreens in science

T2 - A functional data analysis

AU - Zhang, Ruizhi

AU - Wang, Jian

AU - Mei, Yajun

PY - 2017/8/31

Y1 - 2017/8/31

N2 - 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.

AB - 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.

KW - Citation trajectory

KW - Evergreen

KW - Functional Poisson regression

KW - Functional principal component analysis

KW - K-means clustering

U2 - 10.1016/j.joi.2017.05.007

DO - 10.1016/j.joi.2017.05.007

M3 - Journal article

AN - SCOPUS:85020729837

VL - 11

SP - 629

EP - 644

JO - Journal of Informetrics

JF - Journal of Informetrics

SN - 1751-1577

IS - 3

ER -