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The time dimension of science: Connecting the past to the future

Published on May 1, 2017in Journal of Informetrics3.88
· DOI :10.1016/j.joi.2017.04.002
Yian Yin1
Estimated H-index: 1
(NU: Northwestern University),
Dashun Wang14
Estimated H-index: 14
(NU: Northwestern University)
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Abstract
A central question in science of science concerns how time affects citations. Despite the long-standing interests and its broad impact, we lack systematic answers to this simple yet fundamental question. By reviewing and classifying prior studies for the past 50 years, we find a significant lack of consensus in the literature, primarily due to the coexistence of retrospective and prospective approaches to measuring citation age distributions. These two approaches have been pursued in parallel, lacking any known connections between the two. Here we developed a new theoretical framework that not only allows us to connect the two approaches through precise mathematical relationships, it also helps us reconcile the interplay between temporal decay of citations and the growth of science, helping us uncover new functional forms characterizing citation age distributions. We find retrospective distribution follows a lognormal distribution with exponential cutoff, while prospective distribution is governed by the interplay between a lognormal distribution and the growth in the number of references. Most interestingly, the two approaches can be connected once rescaled by the growth of publications and citations. We further validate our framework using both large-scale citation datasets and analytical models capturing citation dynamics. Together this paper presents a comprehensive analysis of the time dimension of science, representing a new empirical and theoretical basis for all future studies in this area.
  • References (82)
  • Citations (8)
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References82
Newest
#1Lovro Šubelj (University of Ljubljana)H-Index: 12
#2Dalibor Fiala (University of West Bohemia)H-Index: 9
#1Satyam Mukherjee (NU: Northwestern University)H-Index: 9
#2Daniel M. Romero (UM: University of Michigan)H-Index: 15
Last.Brian Uzzi (NU: Northwestern University)H-Index: 34
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#1Miguel Guevara (MIT: Massachusetts Institute of Technology)H-Index: 7
#2Dominik Hartmann (MIT: Massachusetts Institute of Technology)H-Index: 5
Last.César A. Hidalgo (MIT: Massachusetts Institute of Technology)H-Index: 22
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#1Roberta Sinatra (NU: Northeastern University)H-Index: 16
#2Dashun Wang (NU: Northwestern University)H-Index: 14
Last.Albert-La szlo Baraba siH-Index: 115
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#1Katalin Orosz (ELTE: Eötvös Loránd University)H-Index: 2
#2Illés J. Farkas (MTA: Hungarian Academy of Sciences)H-Index: 22
Last.Péter Pollner (MTA: Hungarian Academy of Sciences)H-Index: 8
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#1Athen Ma (QMUL: Queen Mary University of London)H-Index: 10
#2Raul J. Mondragon (QMUL: Queen Mary University of London)H-Index: 11
Last.Vito Latora (University of Catania)H-Index: 57
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Cited By8
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#1Yong Huang (WHU: Wuhan University)H-Index: 2
#2Yi Bu (IU: Indiana University Bloomington)H-Index: 3
Last.Wei Lu (WHU: Wuhan University)
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#1Zhongyang He (PSU: Pennsylvania State University)
#2Zhen Lei (PSU: Pennsylvania State University)
Last.Dashun Wang (NU: Northwestern University)H-Index: 14
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#1Binglu WangH-Index: 1
#2Yi BuH-Index: 3
Last.Win-bin HuangH-Index: 2
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#1Raj Kumar Pan (Aalto University)H-Index: 22
#2Alexander M. Petersen (UCM: University of California, Merced)H-Index: 22
Last.Santo Fortunato (IU: Indiana University Bloomington)H-Index: 1
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#1Binglu Wang (PKU: Peking University)H-Index: 1
#2Yi Bu (IU: Indiana University)H-Index: 3
Last.Yang Xu (PKU: Peking University)
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#1Zhuo-Ming Ren (University of Fribourg)H-Index: 2
#2Zhuo-Ming Ren (University of Fribourg)H-Index: 1
Last.Yi-Cheng Zhang (University of Fribourg)H-Index: 42
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#1K. W. Higham (Victoria University of Wellington)H-Index: 2
#2M. Governale (Victoria University of Wellington)H-Index: 2
Last.U. Zülicke (Victoria University of Wellington)H-Index: 16
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