Evaluating paper and author ranking algorithms using impact and contribution awards
作者：
Highlights：
• Paper and author ranking algorithms are compared and evaluated.
• Large test data sets that are based on expert opinions are used.
• Using citation counts is, in general, the best ranking metric to measure high-impact papers.
• Author-level Eigenfactor performs best in ranking high-impact authors.
• Algorithms based on PageRank rank scientifically important papers better.
摘要
Highlights•Paper and author ranking algorithms are compared and evaluated.•Large test data sets that are based on expert opinions are used.•Using citation counts is, in general, the best ranking metric to measure high-impact papers.•Author-level Eigenfactor performs best in ranking high-impact authors.•Algorithms based on PageRank rank scientifically important papers better.
论文关键词：Bibliometrics,Ranking,Citation analysis
论文评审过程：Received 21 September 2015, Accepted 22 January 2016, Available online 16 March 2016, Version of Record 16 March 2016.
论文官网地址：https://doi.org/10.1016/j.joi.2016.01.010
