Abstract
This paper reviews the academic collaboration network in COVID-19 research from January 2020 to December 2021. We first collected publication data from the Covidia (https://covidia.acemap.info) and constructed a coauthorship network. Then, we select network nodes ranking algorithms on importance from three aspects: information transmission in the network, network modularization, and network stability. Through community investigation and node ranking, we analyzed the evolving characteristics of the COVID-19 collaboration network, including the weakening of overall collaboration density, the strengthening of the network modularization trend, a more uniform distribution of researchers' influence, and the network's adaptation to major events such as international cooperation and the emergence of variant strains. Additionally, we investigated the sensitivity of different indices to international cooperation and variant strains, as well as the time delay between major COVID-19-related events and changes in network indices. This analysis provides insights into the dynamics of academic collaboration networks during the pandemic and can be used as a reference for future research in the field, including the effectiveness of different collaboration models and the potential of network analysis in identifying and predicting emerging infectious diseases.
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