Charting mobility patterns in the scientific knowledge landscape

TL;DR

This paper systematically analyzes scientific knowledge mobility patterns across physics, computer science, and mathematics, revealing that researcher movements resemble physical mobility and can be modeled by a gravity model, distinguishing explorers from exploiters.

Contribution

It introduces a formal framework for studying knowledge mobility using low-dimensional embeddings and identifies distinct researcher mobility archetypes.

Findings

Mobility patterns resemble physical movement.

Knowledge flows follow a gravity model.

Two researcher types: explorers and exploiters.

Abstract

From small steps to great leaps, metaphors of spatial mobility abound to describe discovery processes. Here, we ground these ideas in formal terms by systematically studying scientific knowledge mobility patterns. We use low-dimensional embedding techniques to create a knowledge space made up of 1.5 million articles from the fields of physics, computer science, and mathematics. By analyzing the publication histories of individual researchers, we discover patterns of knowledge mobility that closely resemble physical mobility. In aggregate, the trajectories form mobility flows that can be described by a gravity model, with jumps more likely to occur in areas of high density and less likely to occur over longer distances. We identify two types of researchers from their individual mobility patterns: interdisciplinary explorers who pioneer new fields, and exploiters who are more likely to stay within their specific areas of expertise. Our results suggest that spatial mobility analysis is a valuable tool for understanding knowledge evolution.