Quantifying patterns of research-interest evolution

To understand quantitatively how scientists choose and shift their research focus over time is of high importance, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded. Despite extensive investigation into various factors that influence a scientist's choice of research topics, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research-interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, and we show that changes in research interests follow a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research-interest evolution. We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations. This work uncovers and quantitatively analyses macroscopic patterns that govern changes in research interests, thereby showing that there is a high degree of regularity underlying scientific research and individual careers.