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The local structure of citation networks uncovers expert-selected milestone papers
- Source :
- Journal of Informetrics. 15:101220
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Recent works aimed to understand how to identify “milestone” scientific papers of great significance from large-scale 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 impact metrics. Our results reveal that the obtained PageRank-based local metrics outperform the citation count and other local metrics in identifying the seminal papers. Compared with global metrics, these local metrics can reach similar performance in the identification of seminal papers in shorter computational time, without requiring 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.
- Subjects :
- Information retrieval
Computer science
Kleene's recursion theorem
Library and Information Sciences
Network topology
Computer Science::Digital Libraries
Local structure
Computer Science Applications
law.invention
Identification (information)
PageRank
Ranking
law
Milestone (project management)
Citation
Subjects
Details
- ISSN :
- 17511577
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Journal of Informetrics
- Accession number :
- edsair.doi...........ad76d07032adff26a0a95c6efe9efdc0