计算机科学
声望
排名(信息检索)
人气
强化学习
引用
页面排名
钢筋
相互信息
人工智能
度量(数据仓库)
情报检索
数据科学
机器学习
数据挖掘
万维网
心理学
哲学
社会心理学
语言学
作者
Leibao Zhang,Yanli Fan,Wenyu Zhang,Shuai Zhang,Dejian Yu
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2019-03-16
卷期号:36 (2): 1505-1519
被引量:2
摘要
Quantitative methods for determining the quality of scientific publications evolved gradually from popularity methods to prestige methods. However, existing methods have some drawbacks, such as inability to account for important factors and mutual reinforcement between different entities, and limi tation of using novel information techniques like artificial intelligence (AI) methods. This study proposes an intelligent time-aware mutual reinforcement ranking (TAMRR) model that accounts for mutual reinforcement, and temporal factors, such as the time of citation, to measure the prestige of scientific papers. The method also considers the distribution of the co-authors’ contributions, which indicates the credit allocation of citations. Moreover, mutual reinforcement which indicates interactive impact between different entities by means of the extension of an AI algorithm, i.e., Hyperlink-Induced Topics Search (HITS) algorithm, is adopted to further explore the interactions of papers, journals and authors. Another AI algorithm, i.e., PageRank, is also enhanced to measure the prestige of papers, journals, and authors in citation networks, which are then used as the inputs to the modified HITS. Experiments on temporal factors and heterogeneous networks reveal that these factors are likely to be informative in prestige measurements. Analysis of correlations suggests that our proposed intelligent ranking method is reasonable. This study offers an intelligent method for researchers, authors, and entrepreneurs to quantify the importance of scientific papers and the conclusions are likely to be of importance for researchers in both the academic and enterprise domains.
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