  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.
