Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes
The h-index is a citation-based metric with extensive applications, and several variants have been developed to complement it. This study formulates the optimal growth path (OGP) models of selected h-like indexes, that is, the h-index, g-index, A-index, R-index, and e-index, and analyzes their OGP-allocated strategies of citations. It is argued that the OGP is a useful tool for analyzing the sensitivity of these h-like indexes to citations. Through simulation experiments with both real and random data, the sensitivity of the selected h-like indexes to citations is compared. Interestingly, it is found that the h-index performs the worst according to the OGP. Further, it is shown that combining the h-index with the A-index decreases the sensitivity to the citations of the h-index. In summary, this study provides new insights into how to evaluate scientific outputs based on h-like indexes.
The file attached to this record is the author's final peer reviewed version.
Citation : Dong, Y., Chen, M., Guo, Z., Chiclana, F., Herrera-Viedma, E. (2021) Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes. Journal of Informetrics. Vol. 15, Iss. 4, 101215,
ISSN : 1751-1577
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes