# Quantifying patterns of research interest evolution

arXiv: https://arxiv.org/abs/1709.03319 · HTML: https://mlsanity.com/paper/1709.03319/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 8 of 8)

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- [Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach](https://mlsanity.com/paper/2407.14559/) (2024)
- [PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph](https://mlsanity.com/paper/2504.17641/) (2025)
- [Research Method Usage across Academic Ages in Library and Information Science: An Empirical Study (1990-2023)](https://mlsanity.com/paper/2606.21862/) (2026)
- [The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing](https://mlsanity.com/paper/2607.02416/) (2026)

## Abstract (arXiv)

Our quantitative understanding of how scientists choose and shift their research focus over time is highly consequential, 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 investigations of 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, finding that research interest change follows 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 develop a random walk based model, allowing us to accurately reproduce the empirical observations. This work presents a quantitative analysis of macroscopic patterns governing research interest change, discovering a high degree of regularity underlying scientific research and individual careers.