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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (4): 59-70    DOI: 10.11925/infotech.2096-3467.2017.1162
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Ranking Scholarly Impacts Based on Citations and Academic Similarity
Liu Junwan, Yang Bo(), Wang Feifei
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
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Abstract  

[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.

Key wordsCitation Network      Academic Similarity      Academic Influence      Ranking Method     
Received: 20 November 2017      Published: 11 May 2018
ZTFLH:  G353.1  

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1162     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I4/59

施引学者 被引学者 学术相似度 引用频次
姓名 机构 姓名 机构
Durbin Richard Wellcome Trust Sanger Inst Prokopenko Inga Univ Oxford 0.56292 7
Durbin Richard Wellcome Trust Sanger Inst Muzny Donna Baylor Coll Med 0.85074 1
Durbin Richard Wellcome Trust Sanger Inst Raitakari Olli Univ Turku 0.58119 3
Durbin Richard Wellcome Trust Sanger Inst Durbin Richard Wellcome Trust Sanger Inst None 34
Durbin Richard Wellcome Trust Sanger Inst Biesecker Leslie NHGRI 0.61436 1
排名 姓名 PR 排名 姓名 PR
1 boerwinkle, eric 0.004715 11 eriksson, johan g 0.003537
2 de jager, philip l. 0.004254 12 ophoff, roel a 0.003181
3 meitinger, thomas 0.004173 13 raitakari, olli t 0.003118
4 hirschhorn, joel n. 0.003937 14 hakonarson, hakon 0.002978
5 aung, tin 0.003816 15 montgomery, grant w 0.002938
6 alkuraya, fowzan s. 0.003772 16 daly, mark j 0.002913
7 shin, hyoung doo 0.003658 17 munnich, arnold 0.002875
8 majewski, jacek 0.003624 18 de bakker, paul i. w 0.002837
9 robert, catherine 0.003564 19 martin, nicholas g 0.002638
10 palotie, aarno 0.003561 20 illig, thomas 0.002637
数据 操作 时间
数量 单位
训练集数据 数据预处理 3.74 小时
Word2Vec模型训练 7.46 小时
测试集数据 数据预处理 27.13 分钟
TF-IDF运算 2.52 分钟
Auth2Vec学术相似度计算 4.12 分钟
引文网络构建 12.79 分钟
PageRank排名 4.42 分钟
PR值 H指数 特征向量中心度
PR值 Pearson相关系数 1 .617** .872**
显著性(双尾) 0 0 0
姓名 论文数量 总被引频次 平均被引频次 最高单篇被引频次 NatureScience论文
Boerwinkle, Eric 240 14 722 61.34 1 441 15
de Jager, Philip l 97 5 143 53.02 820 11
Meitinger, Thomas 173 13 386 77.38 1 441 11
Hirschhorn, Joel N 146 9 428 64.58 1 441 13
Aung, Tin 53 3 168 59.77 340 1
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