Abstract
In the last few years, it has been noticed that h-index and their variants perceived a lot of attention in scientific world due to some valuable properties (easy to compute, relation between quantity of publications and their significance, and so on). Many other metrics have been proposed in order to enhance and conquer the flaw of the original Hirsch proposal. This paper presents a comprehensive analysis of the h-index and their variants. The initial h-index proposal is studied in detail with its advantages and disadvantages. A brief discussion of many of the h-related indices is discussed along with their mathematical workings and primary characteristics. Some of the major works that analyze and compare them are also presented. The logic of h-index and its variant has been implemented and reviewed to find the author ranking. The results obtained from h-index and its variant are compared using different citation databases (DBLP and ACM library databases). The effect of all the indicators and the variation in the result are analyzed. To find the better and fair result for author ranking, we calculate the average of all the indicators. The results show that averaging h-index variants gives a fair author ranking to compare the works of different authors.
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Chandra, P.K., Jha, V., Abhishek, K. (2020). Effective Author Ranking Using Average of Different h-Index Variants. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_6
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DOI: https://doi.org/10.1007/978-981-15-4032-5_6
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