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数据分析与知识发现  2018, Vol. 2 Issue (4): 59-70     https://doi.org/10.11925/infotech.2096-3467.2017.1162
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于引证行为与学术相似度的学者影响力领域排名方法研究*
刘俊婉, 杨波(), 王菲菲
北京工业大学经济与管理学院 北京 100124
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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摘要 

目的】针对多样化评价指标导致评价体系庞大、计算繁琐、结论模糊等问题, 研究一套公正、有效、快速的学术影响力排名机制。【方法】结合Word2Vec算法、TF-IDF算法和PageRank算法, 提出一种基于引证行为与学术相似度的学者影响力领域排名方法。【结果】改进后的排序算法综合了学者学术关系层面与学者学术产出层面的学术影响力, 在有效性维度表现优异: PR值与特征向量中心度、H指数的相关性分别为0.872、0.617, 对传统评价指标具有优秀的替代作用; 同时, 在固定排名区间内学者的平均H指数与平均被引频次均有所提高, 前百名学者的平均H指数提高1.087, 平均被引频次提高2.080, 排名效果优于原始PageRank算法。【局限】算法时间复杂度与空间复杂度虽然在可接受范围之内, 但相对原始PageRank算法效率有所降低。【结论】改进算法适用于具有大量节点的学者学术网络, 节点PR值随着网络质量扩大而更趋于准确, 因此在多学科、大量学者等场景下的学术影响力评价中, 改进排名算法对原有评价指标具有一定的替代性, 且效果表现较改进前表现优异。

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刘俊婉
杨波
王菲菲
关键词 引文网络学术相似度学术影响力排名方法    
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
收稿日期: 2017-11-20      出版日期: 2018-05-11
ZTFLH:  G353.1  
基金资助:*本文系国家自然科学基金青年项目“共生视角下的院士科学合作网络结构与演化趋势研究: 以中美两国科学院院士为例”(项目编号: 71603015)、国家社会科学基金青年项目“基于多维信息计量分析的学术影响力综合评价研究”(项目编号: 15CTQ023)和北京市自然科学基金项目“基于技术共生网络结构探测和演化的新兴趋势识别研究”(项目编号: 9182001)的研究成果之一
引用本文:   
刘俊婉, 杨波, 王菲菲. 基于引证行为与学术相似度的学者影响力领域排名方法研究*[J]. 数据分析与知识发现, 2018, 2(4): 59-70.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1162      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I4/59
  CBOW模型示意图
  领域排名方法技术路线图
  数据采集与预处理流程图
  Word2Vec训练集输入样例
  指标计算与网络构建流程
  发文量前5 000作者的热点词汇分布
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
  遗传学领域学者间学术相似度样例表
  发文量前5 000学者全作者网络
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
  遗传学领域学者影响力前20排名表
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
  领域排名方法各操作步骤消耗时间量统计表
  特征向量中心度与PR值的散点分布
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
  各指标相关性分析表
  H指数与PR值的散点分布
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
  遗传学领域排名前5的研究学者发文情况统计表
  改进后排序算法与原PageRank算法各排名区间指标差值分布
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