[Objective] This study aims to establish a fair and objective evaluation mechanism for academic impacts, aiming to solve the issues like huge appraisal system, complicated calculation and vague conclusion. [Methods] We proposed a ranking method for each scholar’s impacts based on citation behavior and academic similarity, as well as with the help of Word2Vec, TF-IDF, and PageRank algorithms. [Results] The proposed method combined the influence of a researcher’s scholarly relationship and academic outputs. It has excellent performance in the validity dimension: the relevance of H index and the center of the feature vector with the PR value were 0.872 and 0.617, respectively. The proposed evaluation index could replace the traditional metrics. The average H-index and citation frequency of the scholars within the fixed-ranking interval both increased. The average H-index of the top 100 scholars increased by 1.087 and the average cited frequency increased by 2.080, which were better than the original PageRank algorithm. [Limitations] The efficiency of the proposed algorithm was lower than the PageRank algorithm. [Conclusions] Our new algorithm could be used to analyze academic networks with a large number of nodes. The node’s PR value will be more accurate as the network quality expands. Therefore, the new ranking algorithm could effectively evaluate the academic impacts of many scholars from multi-disciplinary fields, and has better performance than the existing ones.
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Liu Junwan,Yang Bo,Wang Feifei. Ranking Scholarly Impacts Based on Citations and Academic Similarity. Data Analysis and Knowledge Discovery, 2018, 2(4): 59-70.
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