一种基于信誉机制的科学文献影响力评价方法

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 132-137.

一种基于信誉机制的科学文献影响力评价方法

冯磊^1,2, 冀俊忠^1,2, 吴晨生³

北京工业大学多媒体与智能软件技术北京市重点实验室北京100124¹
北京工业大学信息学部北京100124²
北京市科学技术情报研究所北京100048³

出版日期:2019-02-26 发布日期:2019-02-26
通讯作者: 冀俊忠(1969-),男,博士,教授,CCF会员,主要研究方向为机器学习、数据挖掘和群智能算法,E-mail:jjz01@bjut.edu.cn
作者简介:冯磊(1992-),男,硕士生,主要研究方向为复杂网络、数据挖掘;冀俊忠(1969-),男,博士,教授,CCF会员,主要研究方向为机器学习、数据挖掘和群智能算法,E-mail:jjz01@bjut.edu.cn(通信作者);吴晨生(1967-),男,博士,研究员,主要研究方向为科技情报、科学普及。
基金资助:
本文受国家自然科学基金重点项目(613300194)资助。

New Method for Ranking Scientific Publications with Creditworthiness Mechanism

FENG Lei^1,2, JI Jun-zhong^1,2, WU Chen-sheng³

Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology,Beijing University of Technology,Beijing 100124,China¹
Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China²
Beijing Institute of Science and Technology Information,Beijing 100048,China³

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 学术影响力评价一直是文献计量学领域的一个研究热点。已有的大多基于数据挖掘的学术影响力评价方法忽略了恶意活动产生的影响,导致评价结果欠佳。为解决这一问题,提出了一种名为ReputeRank的新方法,该方法采用信誉机制来评估引文网络中出版物的有效性。信誉机制包括3个阶段:种子集选择阶段、信誉传播阶段和集成计算阶段。首先,ReputeRank利用SCI期刊分区信息选择引文网络中潜在的好种子和坏种子;然后,根据信誉传播思想,信誉度良好的种子指向的论文通常具有更高的可信度,而信誉度不好的种子指向的论文通常具有较低的可信度,该方法使用TrustRank和Anti-TrustRank评价公式在引文网络中迭代传播信任值和不信任值;最后,根据引文网络中的信任值和不信任值,利用综合集成公式对每篇论文计算评分,并根据评分结果对所有论文降序排列。在KDD cup 2003数据集上的实验结果表明,与3种影响力评价方法PageRank,CountDegree和SPRank相比,ReputeRank能够获得更优的效果。

关键词: 信息传播, 信誉度, 学术影响力评价, 引文网络

Abstract: Evaluating the scientific value of publications has always been a research focus in the field of bibliometrics.However,some mainstream methods based on data mining overlook the influence of malicious activities and result in poor evaluation results.To solve this problem,this paper proposed a new method named ReputeRank,which employs a creditworthiness mechanism to evaluate the effectiveness of publications in the citation network.The creditworthiness mechanism consists of the seeds selection phase,the spread credit phase and the integrated computation phase.First,ReputeRank employs background information on the division of SCI Periodicals to select potential good seeds and bad seeds in the citation network.Then,in light of assumption that good credibility seeds pointing to papers which usually have a higher credible degree while bad credibility seeds pointing to papers which often have a lower credible degree,the method uses TrustRank and Anti-TrustRank evaluation formula to iteratively spread trust values and distrust values over the citation network.Finally,according to the trust and distrust values in the citation network,the method utilizes an integrated equation to comprehensively compute the score value of each paper and arranges all papers in the descen-ding order of the score values.The experimental results on KDD cup 2003 datasets demonstrate that ReputeRank has good performance of effectiveness and robustness compared with PageRank,CountDegree and SPRank.

Key words: Citation network, Credibility, Evaluation of academic influence, Information dissemination

中图分类号:

TP391

冯磊, 冀俊忠, 吴晨生. 一种基于信誉机制的科学文献影响力评价方法[J]. 计算机科学, 2018, 45(11A): 132-137. https://doi.org/

FENG Lei, JI Jun-zhong, WU Chen-sheng. New Method for Ranking Scientific Publications with Creditworthiness Mechanism[J]. Computer Science, 2018, 45(11A): 132-137. https://doi.org/

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