论文标题
动态时间序列对机构影响的隐性多功能学习预测
Implicit Multi-feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
论文作者
论文摘要
预测研究机构的影响是决策者的重要工具，例如资助机构的资源分配。尽管采用定量指标来衡量研究机构的影响，但鲜为人知的机构的影响如何及时发展。先前的研究重点是利用不同机构的历史相关性得分来预测这些机构的未来影响。在本文中，我们探讨了可以推动机构影响变化的因素，发现该机构的影响（由机构公认的论文数量衡量），更多的是由作者对机构的影响决定。机构特征和州GDP的地理位置可以推动机构影响的变化。确定这些功能使我们能够制定一个预测模型，该模型整合了个人能力，机构位置和状态GDP的影响。该模型揭示了推动机构未来影响的根本因素，这些因素可用于准确预测机构的未来影响。
Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.
