标题： 基于PageRank的作者排名是否优于简单的引用次数？ 显示英文标题

标题： Do PageRank-based author rankings outperform simple citation counts?

摘要： 研究人员的生产力和影响力的基本指标仍然是发表论文的数量及其被引次数。 这些指标清晰、直接且易于获取。 当需要对学者进行排名时，例如在资助、奖项或晋升程序中，它们是最快且最经济的优先选择某些科学家的方法。 然而，由于其性质，存在过度简化科学成就的风险。 因此，提出了许多其他指标，包括使用用于网页排名的PageRank算法及其适用于引用网络的修改版本。 尽管如此，这种递归方法计算成本高昂，即使它具有偏向声望而非流行度的优势，其应用应有充分的理由，尤其是在与标准引用次数比较时。 在本研究中，我们分析了计算机科学领域人工智能、软件工程以及理论与方法三个类别中的三个大型论文数据集，并将12种不同的排名方法应用于作者的引用网络。 我们将所得排名与自行编制的杰出研究人员名单进行比较，这些研究人员被选为该领域权威期刊的常任编辑委员会成员，结论是没有证据表明基于PageRank的方法优于简单的引用次数。 显示英文摘要

摘要： The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors. We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts.