Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes
•We propose the citations optimal growth path (OGP) problems for the selected h-like indexes.•We present some interesting properties of the OGP-allocated strategies of citations.•We compare the sensitivity to citations of the selected h-like indexes.•We analyze the combination of the h-index with an...
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Published in | Journal of informetrics Vol. 15; no. 4; p. 101215 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.11.2021
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Abstract | •We propose the citations optimal growth path (OGP) problems for the selected h-like indexes.•We present some interesting properties of the OGP-allocated strategies of citations.•We compare the sensitivity to citations of the selected h-like indexes.•We analyze the combination of the h-index with another h-like index.
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. |
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AbstractList | •We propose the citations optimal growth path (OGP) problems for the selected h-like indexes.•We present some interesting properties of the OGP-allocated strategies of citations.•We compare the sensitivity to citations of the selected h-like indexes.•We analyze the combination of the h-index with another h-like index.
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. |
ArticleNumber | 101215 |
Author | Guo, Zhaoxia Herrera-Viedma, Enrique Chen, Meiqian Chiclana, Francisco Dong, Yucheng |
Author_xml | – sequence: 1 givenname: Meiqian surname: Chen fullname: Chen, Meiqian email: mqchen@stu.scu.edu.cn organization: Center for Network Big Data and Decision-Making, Business School, Sichuan University, Chengdu 610065, China – sequence: 2 givenname: Zhaoxia surname: Guo fullname: Guo, Zhaoxia email: zx.guo@alumni.polyu.edu.hk organization: Center for Network Big Data and Decision-Making, Business School, Sichuan University, Chengdu 610065, China – sequence: 3 givenname: Yucheng surname: Dong fullname: Dong, Yucheng email: ycdong@scu.edu.cn organization: Center for Network Big Data and Decision-Making, Business School, Sichuan University, Chengdu 610065, China – sequence: 4 givenname: Francisco orcidid: 0000-0002-3952-4210 surname: Chiclana fullname: Chiclana, Francisco email: chiclana@dmu.ac.uk organization: Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, UK – sequence: 5 givenname: Enrique surname: Herrera-Viedma fullname: Herrera-Viedma, Enrique email: viedma@decsai.ugr.es organization: Andalusian Research Institute on Data Science and Computational Intelligence, University of Granada, Granada 18071, Spain |
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Snippet | •We propose the citations optimal growth path (OGP) problems for the selected h-like indexes.•We present some interesting properties of the OGP-allocated... |
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Title | Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes |
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