Scientific impact evaluation and the effect of self-citations: mitigating the bias by discounting h-index

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, Universität 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 Científicas, 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.