Paper Info
Title
Scientific impact evaluation and the effect of self-citations: Mitigating the bias by discounting the h-index.
Abstract
In this article, we propose a measure to assess scientific impact that discounts self-citations and does not require any prior knowledge of their distribution among publications. This index can be applied to both researchers and journals. In particular, we show that it fills the gap of the h-index and similar measures that do not take into account the effect of self-citations for authors or journals impact evaluation. We provide 2 real-world examples: First, we evaluate the research impact of the most productive scholars in computer science (according to DBLP Computer Science Bibliography, Universitat Trier, Trier, Germany); then we revisit the impact of the journals ranked in the Computer Science Applications section of the SCImago Journal & Country Rank ranking service (Consejo Superior de Investigaciones Cientificas, University of Granada, Extremadura, Madrid, Spain). We observe how self-citations, in many cases, affect the rankings obtained according to different measures (including h-index and ch-index), and show how the proposed measure mitigates this effect.
Year
DOI
Venue
2013
10.1002/asi.22976
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
Keywords
Field
DocType
bibliometrics,citation analysis
Data mining,Impact evaluation,Discounting,Ranking,Computer science,Bibliography,Citation analysis,Bibliometrics
Journal
Volume
Issue
ISSN
64
11
1532-2882
Citations 
PageRank 
References 
14
0.89
17
Authors
2
Name
Order
Citations
PageRank
Emilio Ferrara1169292.13
Alfonso E. Romero210910.68