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Disparity in the Evolving COVID-19 Collaboration Network
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 13971 LNCS:331-339, 2023.
Article in English | Scopus | ID: covidwho-2305929
ABSTRACT
COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Year: 2023 Document Type: Article