SocioHGN: A Heterogeneous Graph Networks Augmented by Social Mechanism

SocioHGN: A Heterogeneous Graph Networks Augmented by Social Mechanism | 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 SocioHGN: A Heterogeneous Graph Networks Augmented by Social Mechanism Zhexi Gu, Pengfei Shen, Hao Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6857765/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in EPJ Data Science → Version 1 posted 12 You are reading this latest preprint version Abstract Analyzing online opinion dynamics during controversial global events presents significant challenges. While existing graph learning models identify structural patterns, they overlook key social mechanisms like collective meaning formation and emotional contagion that shape social relationships and community evolution. We propose SocioHGN, an innovative model integrating sociological theory to simulate interactions among users, topics, and geographic locations. Our approach employs frame-aware subgraph extraction combined with graph attention networks to capture complex social dynamics. We validate our model using Weibo data from the Russia-Ukraine conflict, comparing against established graph neural network baselines. Results show SocioHGN significantly outperforms existing methods in link prediction (AUC) and social consistency metrics (RAS, EDI). Moreover, SocioHGN provides interpretable insights, quantitatively revealing how frame evolution drives community polarization, demonstrating its value for both prediction and social analysis. Heterogeneous graph neural networks Social network analysis Sociological embedding Link prediction Frame alignment Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in EPJ Data Science → Version 1 posted Editorial decision: Revision requested 07 Sep, 2025 Reviews received at journal 05 Sep, 2025 Reviews received at journal 06 Aug, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 09 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6857765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472330204,"identity":"747713c3-a27c-4aa4-abbd-ddeea723745c","order_by":0,"name":"Zhexi Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACCSBOYGCQg/DYSNBiTKIWIEhsIFqL/OzmYxIPfh1O33C7+QHDh7LDhLUwzjmWbJDYdzh3w51jBowzzhGhhVkix/BBYg9Qy40cBmbeNiK0sEnkfzgA1JJuANLylxgtPBI5jA8SfhxOAGthJEaLhESasUFiQ7rhzBtpBgd7zqUT1iI/I/mZ5I8/1vJ8N5IfPvhRZk1YCxgwtkHoA0SqB4E/JKgdBaNgFIyCkQcAKSo9XGuHwCYAAAAASUVORK5CYII=","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":true,"prefix":"","firstName":"Zhexi","middleName":"","lastName":"Gu","suffix":""},{"id":472330205,"identity":"2f269bbe-b318-4eb0-8842-1d8838abf19c","order_by":1,"name":"Pengfei Shen","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Shen","suffix":""},{"id":472330206,"identity":"78dcd9bd-f4a3-4c32-8c89-10e109511906","order_by":2,"name":"Hao Chen","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-10 00:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6857765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6857765/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1140/epjds/s13688-025-00604-3","type":"published","date":"2025-12-08T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98243835,"identity":"4f22f604-72d4-40c4-82b8-ab36e5a5b503","added_by":"auto","created_at":"2025-12-15 16:10:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2274265,"visible":true,"origin":"","legend":"","description":"","filename":"SocioHGNAHeterogeneousGraphNetworksAugmentedbySocialMechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6857765/v1_covered_be21d735-5904-4dd7-b85a-5bbd65e649e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SocioHGN: A Heterogeneous Graph Networks Augmented by Social Mechanism","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"epj-data-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epds","sideBox":"Learn more about [EPJ Data Science](https://epjdatascience.springeropen.com/)","snPcode":"13688","submissionUrl":"https://submission.springernature.com/new-submission/13688/3","title":"EPJ Data Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heterogeneous graph neural networks, Social network analysis, Sociological embedding, Link prediction, Frame alignment","lastPublishedDoi":"10.21203/rs.3.rs-6857765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6857765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Analyzing online opinion dynamics during controversial global events presents significant challenges. 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