基于PageRank的合著论文中作者贡献分配算法

随着科研合作越来越普遍，对合著者的贡献如何合理分配提出了挑战。该文提出了一种基于PageRank的论文合著者贡献分配算法(ACA_PR算法)，采用PageRank值和总引用量的加权值度量文章的价值，构建合作者科研记录和科研成果被引情况的共引网络，对论文合著者的贡献进行分配。以美国物理学会APS数据集进行实证研究，通过在诺贝尔物理学奖得主发表的合著论文中识别诺贝尔奖得主验证算法的准确性。实验结果表明，在31篇诺贝尔奖提名论文中，ACA_PR算法的准确率为80.64%。工作在人员聘用、奖励、晋升等方面对评价科研工作者的影响力有着十分重要的作用。

Credit Allocation for Each Author in a Multi-Author Paper Based on PageRank

Credit allocation of each author of a multi-author paper has been a long standing concern. Regarding to the fact that the credit of each paper is not equal, this paper developes an improved credit allocation method, namely ACA_PR method. It uses the PageRank value and total citation of papers as total credit of one paper in order to measure the value of paper, and it constructs a co-citation network of collaborators’ scientific research records and cited research results, and distributes the contributions of the co-authors of the papers. By distinguishing the laureates of the Nobel Prize in Physics from the authors of prize-winning papers in American Physical Society (APS) dataset, this paper validates the ACA_PR method. Result shows that the ACA_PR method outperforms the state-of-the-art methods, and the accuracy of identifying the Nobel Prize laureates in Physics is 80.64% for 31 multi-author prize-winning papers in APS dataset. Accurate assessment the credit of researchers is significant in many aspects, such as hiring, funding and promotion, etc.