Learning-Compatible Sparse Hypergraph Partitioning for Scalable Structured Prediction

Learning-Compatible Sparse Hypergraph Partitioning for Scalable Structured Prediction | 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 Learning-Compatible Sparse Hypergraph Partitioning for Scalable Structured Prediction Menghao Li, Yuhan Chen, Zixuan Wang, Liyun Xu, Zhou Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7994877/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 In this paper, we introduce a novel framework for learning-compatible sparse hypergraph partitioning aimed at enhancing scalable structured prediction. Traditional hypergraph partitioning approaches typically rely on cut-based criteria, which often misalign with task-specific learning objectives, leading to suboptimal performance in applications such as natural language processing and computer vision. Our proposed method reformulates the partitioning problem to directly incorporate learning goals through a two-pronged optimization strategy that applies spectral and convex relaxation techniques. We provide a thorough theoretical analysis, establishing generalization and approximation bounds for our approach. Empirical evaluations conducted on benchmark datasets reveal significant improvements in prediction accuracy and computational efficiency compared to traditional methods. By bridging the gap between hypergraph partitioning and structured prediction, our work not only advances the state of the art in artificial intelligence but also sets the stage for future research on integrating learning-directed optimizations with complex structured tasks. Overall, our contributions pave the way for more adaptable and effective models capable of addressing the evolving challenges in the landscape of AI-driven predictions. Hypergraph Partitioning Structured Prediction Sparse Optimization Spectral Methods End-to-End Learning Convex Relaxation 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. 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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-7994877","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537850609,"identity":"2b124bc9-5a69-4f07-bb7f-03c00ad9d03e","order_by":0,"name":"Menghao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Menghao","middleName":"","lastName":"Li","suffix":""},{"id":537850610,"identity":"00b47a07-4142-4c79-8136-6e936c56b2be","order_by":1,"name":"Yuhan Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Chen","suffix":""},{"id":537850611,"identity":"48eaa011-14b1-4fa6-96cc-aeba9c8d3b2c","order_by":2,"name":"Zixuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Wang","suffix":""},{"id":537850612,"identity":"2d390382-0733-4779-875e-c95a39457b5e","order_by":3,"name":"Liyun Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Xu","suffix":""},{"id":537850613,"identity":"b0bc55b2-4a4c-4743-9574-6f024133fa7a","order_by":4,"name":"Zhou Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDACCSBmbEASMGAH8QwsSNHCcwBESpCiRSIBJo4dyM9uPvbw5w6bxAbpw4c/fKi5J28u+fzqhh8FEgz87d0J2LQY3DmWbiB5Ji2xgS8tTXLGsWLDnbNzym72AB0mcebsBqxaJHLMJAzbDic28PCYMfOwJSQY3M5Ju8ED1GIgkYtVi/yM/G8SiWAt/J8///kH1HLzTNrNP3i0MNzIYZM4CLGFQZqxDajlBvux2/hsMbiRZibZ2JZm3MbDZibZ25dguOFMDtttGQMJHlx+kZ+R/EzyZ5uNbD8P8+MPP74lyBscP/7s5ps/NnL87b3YHQYDbAgmjwGYxKscDbA/IEX1KBgFo2AUDH8AAA8DYpsNUdNBAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-10-31 06:06:21","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7994877/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7994877/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95228975,"identity":"1e0da428-002b-4f53-8f74-eeb35853a903","added_by":"auto","created_at":"2025-11-05 16:34:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":247172,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7994877/v1_covered_64150896-7a1e-439a-9f6c-fc1a3f1a6989.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLearning-Compatible Sparse Hypergraph Partitioning for Scalable Structured Prediction\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Hypergraph Partitioning, Structured Prediction, Sparse Optimization, Spectral Methods, End-to-End Learning, Convex Relaxation","lastPublishedDoi":"10.21203/rs.3.rs-7994877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7994877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we introduce a novel framework for learning-compatible sparse hypergraph partitioning aimed at enhancing scalable structured prediction. 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