Article

Cracking the code of co-authorship networks geo-temporally using interpretable machine learning

2025-03-07

Abstract excerpt

An exponential growth in the scientific literature necessitates the development of highly scalable computational tools that can effectively analyze and distill insights from complex, interconnected research landscapes. We introduce Distributed, Interpretable, and Scalable computing for Co-authorship Networks (DISCo-Net), a robust and scalable tool engineered to curate and examine large-scale co-authorship networks...

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Literature Corpus work: cd125865-dffd-56a2-a3d0-9c75a91bd301
DOI: 10.1101/2025.03.05.641725

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