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识别异常引用以客观评估学术文章影响力。

Identifying Anomalous Citations for Objective Evaluation of Scholarly Article Impact.

作者信息

Bai Xiaomei, Xia Feng, Lee Ivan, Zhang Jun, Ning Zhaolong

机构信息

School of Software, Dalian University of Technology, Dalian 116621, China.

School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

出版信息

PLoS One. 2016 Sep 8;11(9):e0162364. doi: 10.1371/journal.pone.0162364. eCollection 2016.

Abstract

Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity.

摘要

评估一篇学术文章的影响力具有重要意义,并且已经引起了广泛关注。尽管基于引用的评估方法已被广泛使用,但这些方法存在局限性,例如在识别异常引用模式方面。这种疏忽将不可避免地导致文章影响力评估的不公平和不准确。在本研究中,为了发现异常引用并确保研究成果评估的公平性和准确性,我们使用以下因素研究文章之间的引用关系:合作次数、合作时间跨度、被引用次数和引用时间跨度,以削弱引用网络中利益冲突(COI)的关系。同时,我们研究一种特殊的利益冲突,即疑似利益冲突关系。基于利益冲突关系,我们进一步提出了COIRank算法,这是一种准确评估文章影响力的创新方案。我们的方法区分引用强度,并利用PageRank和HITS算法对学术文章进行综合排名。实验是在美国物理学会(APS)数据集上进行的。我们发现,在26366篇文章中,约80.88%的文章包含共同作者的贡献引用,其中75.55%的文章被同一机构的作者引用,这表明在客观评估科学论文影响力时,利益冲突和疑似利益冲突不容忽视。此外,我们的实验结果表明,COIRank算法明显优于现有解决方案。我们方法的有效性通过使用推荐强度概率得到验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1507/5015995/ae758fd1530a/pone.0162364.g001.jpg

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