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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | The local structure of citation networks uncovers expert-selected milestone papers |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Journal of Informetrics |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 1751-1577 |
| Volume | 15 |
| Number | 4 |
| Page Range | 101220 |
| Date | 2021 |
| Abstract Text | Recent works aimed to understand how to identify “milestone” scientific papers of great significance from largescale citation networks. To this end, previous results found that global ranking metrics that take into account the whole network structure (such as Google’s PageRank) outperform local metrics such as the citation count. Here, we show that by leveraging the recursive equation that defines the PageRank algorithm, we can propose a family of local versions of PageRank with finite iterations. Our results reveal that these PageRank-based local metrics outperform the citation count and other local metrics in identifying the seminal papers, and compared with global metrics, these local metrics can reach similar performance in the identification of seminal papers with less time overhead and no requirement for the whole network topology. Our findings could help to better understand the nature of groundbreaking research from citation network analysis and find practical applications in large-scale data. |
| Related URLs | |
| Digital Object Identifier | 10.1016/j.joi.2021.101220 |
| Other Identification Number | merlin-id:21641 |
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