# Prediction of next career moves from scientific profiles

arXiv: https://arxiv.org/abs/1802.04830 · HTML: https://mlsanity.com/paper/1802.04830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://mlsanity.com/paper/2302.09938/) (2023)

## Abstract (arXiv)

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.