Predicting citation patterns: defining and determining influence

David Guy Brizan (), Kevin Gallagher, Arnab Jahangir and Theodore Brown
Additional contact information
David Guy Brizan: CUNY and CUNY Graduate Center
Kevin Gallagher: NYU Tandon School of Engineering
Arnab Jahangir: Hunter College CUNY
Theodore Brown: CUNY and CUNY Graduate Center

Scientometrics, 2016, vol. 108, issue 1, No 9, 183-200

Abstract: Abstract Definitions for influence in bibliometrics are surveyed and expanded upon in this work. On data composed of the union of DBLP and CiteSeer x , approximately 6 million publications, a relatively small number of features are developed to describe the set, including loyalty and community longevity, two novel features. These features are successfully used to predict the influential set of papers in a series of machine learning experiments. The most predictive features are highlighted and discussed.

Keywords: Citation analysis; Bibliometrics; Big data; Machine learning (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-016-1950-1 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:108:y:2016:i:1:d:10.1007_s11192-016-1950-1

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

DOI: 10.1007/s11192-016-1950-1

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 ().