根据科学数据预测未来的职业发展
Prediction of next career moves from scientific profiles
摘要：
制度变迁是科学家的关键职业决策，它对教育、科学生产力和科学知识的生成都有重要作用。然而，我们对影响搬迁决定的因素的了解非常有限。在本文中，我们研究了科学家的科学形象如何决定他们的移动决定（即，改变体制）。为此，我们描述了一个科学家的个人资料的三个主要方面：科学家的最近的科学生涯，他们的科学环境的质量和他们的科学合作网络的结构。然后，我们设计并实现了一个两阶段的预测模型：首先，我们使用数据挖掘来预测哪些研究人员将在未来一年的基础上，他们的科学概况;第二，我们预测他们将选择使用一种新的社会引力模型，适应传统的重力模型的人类流动性。在大量科学出版物数据集上的实验表明，我们的方法在这两个阶段都表现良好，与最先进的方法相比，预测误差减少了85%。
英文摘要：
Changing institution is a scientist's key career decision, which plays an important role in education, scientific productivity, and the generation of scientific knowledge. Yet, our understanding of the factors influencing a relocation decision is very limited. In this paper we investigate how the scientific profile of a scientist determines their decision to move (i.e., change institution). To this aim, we describe a scientist's profile by three main aspects: the scientist's recent scientific career, the quality of their scientific environment and the structure of their scientific collaboration network. We then design and implement a two-stage predictive model: first, we use data mining to predict which researcher will move in the next year on the basis of their scientific profile; second we predict which institution they will choose by using a novel social-gravity model, an adaptation of the traditional gravity model of human mobility. Experiments on a massive dataset of scientific publications show that our approach performs well in both the stages, resulting in a 85% reduction of the prediction error with respect to the state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
