1
|
Murray D, Yoon J, Kojaku S, Costas R, Jung WS, Milojević S, Ahn YY. Unsupervised embedding of trajectories captures the latent structure of scientific migration. Proc Natl Acad Sci U S A 2023; 120:e2305414120. [PMID: 38134198 PMCID: PMC10756268 DOI: 10.1073/pnas.2305414120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/13/2023] [Indexed: 12/24/2023] Open
Abstract
Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances, and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.
Collapse
Affiliation(s)
- Dakota Murray
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN47408
- Network Science Institute at Northeastern University, Boston, MA02115
| | - Jisung Yoon
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN47408
- Kellogg School of Management & Organizations at Northwestern University, Evanston, IL60208
- Northwestern Institute on Complex Systems, Evanston, IL60208
| | - Sadamori Kojaku
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN47408
| | - Rodrigo Costas
- Centre for Science and Technology Studies, Leiden University, 2300 AXLeiden, The Netherlands
- Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy, Stellenbosch University, Stellenbosch7600, South Africa
| | - Woo-Sung Jung
- Department of Physics, Pohang University of Science and Technology, Pohang37673, South Korea
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang37673, South Korea
| | - Staša Milojević
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN47408
| | - Yong-Yeol Ahn
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN47408
| |
Collapse
|