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

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.

Methods to Account for Citation Inflation in Research Evaluation

“…The problem is rather simplewhen citations are produced in distinct historical periods their 'nominal values' are inconsistent and thus cannot simply be added together. The ramifications of this problem have been noted in recent work analyzing the citation dynamics of individual careers and publications across the citation life-cycle (Petersen et al, 2014a;Parolo et al, 2015;Yin and Wang, 2017;Pan et al, 2018). To the extent that citation inflation is evident even over such short time periods, aggregating citation counts across several decades even further exacerbates this statistical measurement error.…”

Structure-oriented prediction in complex networks

“…One can easily understand citation indicators at the time of prediction, namely the number of papers, the total number of citations, the career length, the average number of published papers per year, the average annual citations, the annual citations at the time of prediction, the average citations per paper. Yin et al [296] took a large-scale quantitative analysis on how time affects citations, and developed a new theoretical framework to reconcile the interplay between temporal decay of citations and the growth of science. More specifically, Mazloumian et al [297] devised an approach to predicting scholars' scientific impact, which estimated for scholar s the citations to a certain subset of his papers (selected by time-window w) in k subsequent years as shown in Fig.…”

Document- and Keyword-based Author Co-citation Analysis

“…From the perspective of the author level, Yan and Ding (2010) concluded that methods of distributing weights for authors could be divided into three types, including straight counting where only the first author's contribution is acknowledged; unit counting where each coauthor's contribution is counted equally; and adjusted counting where each coauthor's contribution is divided based on the number of coauthors. Furthermore, the time of each citing paper is of much significance for evaluating the citation impact (Yin & Wang, 2017;Wang, Song, & Barabási, 2013). The Discounted Cumulated Impact (DCI) Index, for example, utilized a decay parameter devaluing old citations additionally to consider the influence of time, allowing weighing of the citations by the citation weight value of the citing publication (Järvelin & Persson, 2008).…”