Supply Chain Disruption Risk Prediction Based on Hypergraph Representation and Dynamic Relational-Attentive

Supply Chain Disruption Risk Prediction Based on Hypergraph Representation and Dynamic Relational-Attentive | 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 Research Article Supply Chain Disruption Risk Prediction Based on Hypergraph Representation and Dynamic Relational-Attentive Jinlong Wang, Qixin Zhao, Yingmin Liu, Pengjun Li, Yuanyuan Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6843359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Traditional supply chain risk prediction methods, relying on historical data, expert judgment, scenario analysis, and simulation, exhibit limitations in handling sudden events and high uncertainty within complex systems. Typically leveraging historical semantic links in knowledge graphs, these methods forecast future relational facts among companies. To address these shortcomings, we construct a supply chain risk knowledge graph integrating multi-dimensional enterprise features. We propose a novel Hypergraph Dynamic Graph Attention Neural Network (HG-DRA) for disruption risk prediction. HG-DRA employs hypergraph representation learning and a dynamic relational attention mechanism. Experiments demonstrate that HG-DRA, by effectively integrating operational features, cluster characteristics, and complex heterogeneous graph relationships, outperforms existing machine learning and graph representation learning approaches in identifying supply chain disruption characteristics. Supply chain Disruption risk prediction Knowledge graph Hypergraph Representation Learning Dynamic relational attention Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 04 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 07 Jun, 2025 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. 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