SocioHGN: A Heterogeneous Graph Network Augmented by Social Mechanism

SocioHGN: A Heterogeneous Graph Network 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A Heterogeneous Graph Network Augmented by Social Mechanism Zhexi Gu, Pengfei Shen, Hao Chen This is a preprint; it has not been peer reviewed by a journal.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-6913514/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 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. Social network analysis Heterogeneous graph neural networks Sociological embedding Link prediction Frame alignment Full Text Additional Declarations The authors declare no competing interests. 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. 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-6913514","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472509432,"identity":"5c549d1c-7c5a-4625-9cb4-af4ff10ef18e","order_by":0,"name":"Zhexi Gu","email":"","orcid":"","institution":"University of North Carolina At Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Zhexi","middleName":"","lastName":"Gu","suffix":""},{"id":472509433,"identity":"14cdd592-cf65-4f24-9840-cace07f4726a","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":472509434,"identity":"a0f71342-71fe-48d3-a4b5-4fa01aea6de8","order_by":2,"name":"Hao Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACCR4gUQHnMhOr5QzJWhjbSNHCP7v34IeP8+zsGSRyD35gqLBObCBoyZ1zyZIztyUnNkjkJUswnEknrMVAIsdAmnfbgQQGiRwzoAsPE6XF+DfvnAP2EC3/iNNiJs3bcICxAaylgQgtEjdyzCxnHEtObON5YyyRcCzdmKAW/hk5xjc+1NjZ87PnGH74UGMtS1ALHLCBiASilY+CUTAKRsEowAsA8A41j/rzgLEAAAAASUVORK5CYII=","orcid":"","institution":"University of North Carolina At Chapel Hill","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-17 10:52:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6913514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6913514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84850893,"identity":"cb7f6a15-a715-40a9-8d23-9577f751ddaf","added_by":"auto","created_at":"2025-06-18 04:20:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2303708,"visible":true,"origin":"","legend":"","description":"","filename":"SocioHGNAHeterogeneousGraphNetworkAugmentedbySocialMechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913514/v1_covered_3d7ef3a8-c067-4014-9f79-59214087a88d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSocioHGN: A Heterogeneous Graph Network Augmented by Social Mechanism\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of North Carolina at Chapel Hill","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social network analysis, Heterogeneous graph neural networks, Sociological embedding, Link prediction, Frame alignment","lastPublishedDoi":"10.21203/rs.3.rs-6913514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6913514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalyzing online opinion dynamics during controversial global events presents significant challenges. 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