, Volume 108, Issue 1, pp 183–200

Predicting citation patterns: defining and determining influence

  • David Guy Brizan
  • Kevin Gallagher
  • Arnab Jahangir
  • Theodore Brown

DOI: 10.1007/s11192-016-1950-1

Cite this article as:
Brizan, D.G., Gallagher, K., Jahangir, A. et al. Scientometrics (2016) 108: 183. doi:10.1007/s11192-016-1950-1


Definitions for influence in bibliometrics are surveyed and expanded upon in this work. On data composed of the union of DBLP and CiteSeerx, 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.


Citation analysis Bibliometrics Big data Machine learning 

Funding information

Funder NameGrant NumberFunding Note
National Science Foundation
  • CNS-0958379
  • CNS-0855217
  • ACI-1126113

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  • David Guy Brizan
    • 1
  • Kevin Gallagher
    • 2
  • Arnab Jahangir
    • 3
  • Theodore Brown
    • 1
  1. 1.Department of Computer ScienceCUNY and CUNY Graduate CenterNew YorkUSA
  2. 2.Department of Computer ScienceNYU Tandon School of EngineeringBrooklynUSA
  3. 3.Department of Computer ScienceHunter College CUNYNew YorkUSA

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