Unsupervised embedding of trajectories captures the latent structure of scientific migration
Paper Unwind Series
Dakota Murray
Assistant Research Professor
Past Talk
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Thursday
Feb 22, 2024
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5:30 pm
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177 Huntington Ave.
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*This talk is part of NetSI's Paper Unwind series and is reserved for NetSI members only.

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.

About the speaker
About the speaker

Dakota is a research assistant professor with the Network Science Institute. His research examines the human side of discovery and innovation using data science and computational techniques. His work has examined the role of bias, mobility, and conflict in the production of knowledge in the sciences. Prior to joining the Network Science Institute, Dakota was a data scientist with Digital Science, and a doctoral student at Indiana University.