Ranking scientific publications considering the aging characteristics of citations

Abstract

Ranking the significance of scientific publications has been a challenging topic for a long time. So far, many ranking methods have been proposed, one of which is the well-known PageRank algorithm. In this paper, we introduce aging characteristics to the PageRank algorithm via considering only the first 10 year citations when aggregating resource from different nodes. The validation of our new method is performed on the data of American Physical Society journals. The results indicate that taking into account aging characteristics improves the performance of the PageRank algorithm in terms of ranking accuracy for both papers and authors. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems and similar improvements are expected.

Bibliometric Analysis on Geriatric Nursing Research in Web of Science (1900–2020)

“…In addition, it shows that the flow of geriatric nursing information has been higher during this period. Since citations increase over time, older articles have a longer time to gain citations and have more advantage over their young peers [ 43 ]. No rapid changes were observed in this field.…”

The local structure of citation networks uncovers expert-selected milestone papers

“…This narrow set of historically-significant papers has been the subject of several subsequent works, e.g., Ren et al (2018); Bornmann and Tekles (2020); Fontana et al (2020). A stream of prior works have indicated that these seminal works are better identified by global network-based metrics than by citation count (Mariani et al, 2016;Ren et al, 2018;Jiang and Zhuge, 2019;Wang et al, 2019;Ren, 2019;Xu et al, 2020), and better early detected by time-normalized metrics than by the raw metrics (Mariani et al, 2016;Dunaiski et al, 2019;Wang et al, 2019). Similar observations hold for the identification of expert-selected historically-significant patents (Mariani et al, 2019).…”

Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network

“…Believing that immediate citations after publication is an indicator of good quality, some researchers allocated heavy weights to those papers that are cited shortly after publication (Yan & Ding, 2010;Zhang & Wu, 2018;Zhang et al, 2019a). For alleviating the ranking bias towards newly published papers, Walker et al (2006) and Dunaiski et al (2016) allocated heavier weights to newer papers, while Wang et al (2019) considered the citations in the first 10 years of any paper since its publication and ignored the later ones. Self-citation, which is given a lighter weight than a "normal" citation, is investigated in (Bai et al, 2016).…”

“…It is generally accepted that a good ranking algorithm should be effective in identifying all the influential entities in a comprehensive style (Wang et al, 2019). For the ranked list from a given ranking method, we find out the ranking positions of all those influential entities (e.g., all the papers in GoldP) and calculate the average rank and median rank of them.…”