探索学术网络中新星预测的作者、文章和地点特征集

IF 1.2 4区 管理学 N/A HUMANITIES, MULTIDISCIPLINARY Journal of Scholarly Publishing Pub Date : 2023-06-23 DOI:10.3138/jsp-2022-0025
Amber Urooj, H. Khan, Saqib Iqbal, M. Alghobiri
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引用次数: 0

摘要

冉冉升起的新星是研究领域相对较新的研究人员,发表的研究论文较少,但他们的研究工作水平很高,在不久的将来有可能成为顶尖的研究人员。对研究人员的评价和预测后起之秀的研究之所以受到关注,是因为它对选拔有能力的人才、聘用年轻的研究所教员、寻找期刊和会议的审稿人以及各种委员会的委员都有帮助。在这项研究中,作者解决了寻找新星的研究问题,并在文章、作者和地点三类不同的特征集中提出了新颖的特征。真实世界的数据集已被提取,预处理,并从科学网络用于实证分析。几种不同的监督机器学习、集成学习算法和深度学习应用于数据集。使用分类器将结果与标准性能评价指标进行比较,以揭示所提出的特征和现有特征的重要性。它还表明,小说在寻找新星方面发挥了重要作用。基于集成的机器学习分类器广义线性模型优于所有其他分类器,并且与其他模型和相关文献中的现有研究相比,具有最高的准确率和F-measure。
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Exploring Author, Article, and Venue Feature Sets for Rising Star Prediction in Academic Network
Rising stars are the researchers who are relatively new to the research area and have published fewer research articles, but their research work is of such standard that they have the potential to be top researchers in near future. Research work on the evaluation of researchers and prediction of rising stars is getting attention because it can be useful for selecting capable candidates for the jobs, hiring young faculty members for institutes, and seeking reviewers for journals and conferences and members for different committees. In this research study, the authors address the research problem of finding rising stars and propose novel features in diverse feature sets of three categories: article, author, and venue. The real-world data set has been extracted, preprocessed, and used from the Web of Science for empirical analysis. Several diverse supervised machine learning, ensemble learning algorithms, and deep learning are applied to the data set. The results, using classifiers, are compared based on standard performance evaluation measures to reveal the significance of the proposed as well as existing features. It also shows that the novel features play a significant role in finding rising stars. The ensemble- based machine learning classifier generalized linear model outperforms all other classifiers and gives the highest accuracy and F-measure compared to other models and the existing studies in the relevant literature.
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来源期刊
CiteScore
2.50
自引率
15.40%
发文量
12
期刊介绍: For more than 40 years, the Journal of Scholarly Publishing has been the authoritative voice of academic publishing. The journal combines philosophical analysis with practical advice and aspires to explain, argue, discuss, and question the large collection of new topics that continually arise in the publishing field. JSP has also examined the future of scholarly publishing, scholarship on the web, digitization, copyright, editorial policies, computer applications, marketing, and pricing models. It is the indispensable resource for academics and publishers that addresses the new challenges resulting from changes in technology and funding and from innovations in production and publishing.
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