Scientific collaboration is getting tremendous attention from scholars and becoming the most common way of producing research works from different disciplines, enabling them to solve complex problems. Nevertheless, when the number of collaborators increases in research work, it becomes challenging to single out and recognize one scholar who contributes the most to the collaboration team of multiauthored publications. Hence, determining an influential author either from multiauthored papers or co-authorship networks is an interesting research problem. To address these problems, we develop a citation and similarity-based author ranking method, namely CLARA, that captures the influential author in multiauthored publications. The method considers attributes of publications such as citing papers and co-cited papers and similarity between publications. Firstly, the method computes the contribution of the co-authors in a given paper by employing fractional counting metrics. Secondly, it computes the contextual similarity between the given paper and its co-cited papers. Finally, the method ranks each co-author using the mathematically defined metric, called KeyScore, and discovers the “key” author among the co-authors of the given paper. We validate our method by extracting the papers of the “Chinese Outstanding Youth” winning researchers from the Microsoft Academic Graph dataset. The experimental results show that the CLARA method performs well in identifying key authors accurately and effectively, despite the position of the authors in the author list of their corresponding papers.
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This work is partially supported by the Fundamental Research Funds for the Central Universities under Grant No. DUT22RC(3)060.
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Bedru, H.D., Zhang, C., Xie, F. et al. CLARA: citation and similarity-based author ranking. Scientometrics 128, 1091–1117 (2023). https://doi.org/10.1007/s11192-022-04590-5