Charting mobility patterns in the scientific knowledge landscape
February 25, 2023 Β· Declared Dead Β· π EPJ Data Science
"No code URL or promise found in abstract"
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Authors
Chakresh Kumar Singh, Liubov Tupikina, Fabrice LΓ©cuyer, Michele Starnini, Marc Santolini
arXiv ID
2302.13054
Category
physics.soc-ph
Cross-listed
cs.DL,
cs.SI
Citations
11
Venue
EPJ Data Science
Last Checked
3 months ago
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.
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