论文标题
预测研究人员合着者的数量：一种学习模型
Predicting the number of coauthors for researchers: A learning model
论文作者
论文摘要
预测研究人员的合着者人数有助于了解团队科学的发展。但是，由于研究人员的协作模式多样性，这是一项难以捉摸的任务。这项研究为该变量的动力学提供了学习模型。这些参数是从经验数据中学到的，这些经验数据包括出版物数量和在给定时间间隔的合着者数量。该模型是基于年度新合着者的年数与鉴于年度出版物数量的时间之间的关系，年度出版物数量与鉴于历史数量出版物的时间之间的关系以及Lotka的定律。通过将其应用于高质量DBLP数据集中，可以验证模型的假设。通过令人满意的配件对研究人员合着者数量的进化趋势，该变量的分布以及协作事件的发生概率来测试模型的有效性。由于其回归性质，该模型具有扩展的潜力来评估预测结果的置信度，因此对其他经验研究具有适用性。
Predicting the number of coauthors for researchers contributes to understanding the development of team science. However, it is an elusive task due to diversity in the collaboration patterns of researchers. This study provides a learning model for the dynamics of this variable; the parameters are learned from empirical data that consist of the number of publications and the number of coauthors at given time intervals. The model is based on relationship between the annual number of new coauthors and time given an annual number of publications, the relationship between the annual number of publications and time given a historical number of publications, and Lotka's law. The assumptions of the model are validated by applying it on the high-quality dblp dataset. The effectiveness of the model is tested on the dataset by satisfactory fittings on the evolutionary trend of the number of coauthors for researchers, the distribution of this variable, and the occurrence probability of collaboration events. Due to its regression nature, the model has the potential to be extended to assess the confidence level of the prediction results and thus has applicability to other empirical research.
