Large-Scale Behavioral Network Analysis: Unveiling the Impact of the COVID-19 Pandemic on University Student Interactions

Large-Scale Behavioral Network Analysis: Unveiling the Impact of the COVID-19 Pandemic on University Student Interactions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Large-Scale Behavioral Network Analysis: Unveiling the Impact of the COVID-19 Pandemic on University Student Interactions Zhu Su, Fuqiang Li, Xinlong Bao, Zhicheng Dai, Jianwen Sun, Zhi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7975408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the structural evolution and behavioral regularity of student social networks before, during, and after COVID-19. Using 65 million campus smart-card transactions (2019--2024), we constructed temporal friendship networks and applied motif detection with behavioral orderliness analysis. Results show stage-specific impacts of the pandemic. During Spring 2020, clustering coefficients, entropy, and average degree dropped sharply, reflecting a collapse of established social structures. Within several months, networks largely rebounded, with 4-clique motifs (M3) indicating reliance on tight cliques for support. Under normalized restrictions (2021--2022), a hybrid structure of ``high clustering--long path'' emerged, combining offline recovery with persistent digital ties. Post-pandemic (2023--2024), networks became more fragmented, though M3 motifs persisted, suggesting durable reorganization. Behavioral orderliness followed a parallel trajectory: rapid decline in 2020, partial recovery in 2021--2022, and convergence at higher levels after reopening. Students whithin high-frequency dyads displayed greater orderliness, underscoring a potential bidirectional relationship between stable ties and routine. These findings highlight the fragility, adaptability, and reconstructive capacity of student social systems, offering data-driven evidence for designing resilience strategies in higher education under future crises. Humanities/Complex networks Social science/Complex networks Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Social science/Science technology and society COVID-19 social networks motif analysis orderliness Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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