A data analytic approach to quantifying scientific impact

Citation is perhaps the mostly used metric to evaluate the scientific impact of papers. Various measures of the scientific impact of researchers and journals rely heavily on the citations of papers. Furthermore, in many practical applications, people may need to know not only the current citations of a paper, but also a prediction of its future citations. However, the complex heterogeneous temporal patterns of the citation dynamics make the predictions of future citations rather difficult. The existing state-of-the-art approaches used parametric methods that require long period of data and have poor performance on some scientific disciplines. In this paper, we present a simple yet effective and robust data analytic method to predict future citations of papers from a variety of disciplines. With rather short-term (e.g., 3 years after the paper is published) citation data, the proposed approach can give accurate estimate of future citations, outperforming state-of-the-art prediction methods significantly. Extensive experiments confirm the robustness of the proposed approach across various journals of different disciplines.

Fecha publicación: 02/05/2016
Autor: Xuanyu Cao, , Yan Chen, K.J. Ray Liu
Referencia: Journal of Informetrics. Volume 10, Issue 2, May 2016, Pages 471–484

Enlace original: Journal of Informetrics