CLARA: citation and similarity-based author ranking

Hayat D. Bedru (), Chen Zhang (), Feng Xie (), Shuo Yu () and Iftikhar Hussain ()
Additional contact information
Hayat D. Bedru: Central South University
Chen Zhang: Dalian University of Technology
Feng Xie: National University of Defense Technology
Shuo Yu: Dalian University of Technology
Iftikhar Hussain: University of Science and Technology of China

Scientometrics, 2023, vol. 128, issue 2, No 9, 1117 pages

Abstract: Abstract Scientific collaboration is getting tremendous attention from scholars and becoming the most common way of producing research works from different disciplines, enabling them to solve complex problems. Nevertheless, when the number of collaborators increases in research work, it becomes challenging to single out and recognize one scholar who contributes the most to the collaboration team of multiauthored publications. Hence, determining an influential author either from multiauthored papers or co-authorship networks is an interesting research problem. To address these problems, we develop a citation and similarity-based author ranking method, namely CLARA, that captures the influential author in multiauthored publications. The method considers attributes of publications such as citing papers and co-cited papers and similarity between publications. Firstly, the method computes the contribution of the co-authors in a given paper by employing fractional counting metrics. Secondly, it computes the contextual similarity between the given paper and its co-cited papers. Finally, the method ranks each co-author using the mathematically defined metric, called KeyScore, and discovers the “key” author among the co-authors of the given paper. We validate our method by extracting the papers of the “Chinese Outstanding Youth” winning researchers from the Microsoft Academic Graph dataset. The experimental results show that the CLARA method performs well in identifying key authors accurately and effectively, despite the position of the authors in the author list of their corresponding papers.

Keywords: Node ranking; Citation analysis; Co-authorship network; Scientific collaboration; Scholarly big data (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-022-04590-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:128:y:2023:i:2:d:10.1007_s11192-022-04590-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-022-04590-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().