Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians

Vittorio Fuccella (), Domenico De Stefano (), Maria Prosperina Vitale () and Susanna Zaccarin ()
Additional contact information
Vittorio Fuccella: University of Salerno
Domenico De Stefano: University of Trieste
Maria Prosperina Vitale: University of Salerno
Susanna Zaccarin: University of Trieste

Scientometrics, 2016, vol. 107, issue 1, No 9, 167-184

Abstract: Abstract The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.

Keywords: Bibliographic data source; Record linkage; Author name disambiguation; Scientific collaboration; Co-authorship network (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-016-1872-y 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:107:y:2016:i:1:d:10.1007_s11192-016-1872-y

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

DOI: 10.1007/s11192-016-1872-y

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 ().