DGSTD: Learning on Dynamic Graph with Spatio-Temporal Disentanglement

DGSTD: Learning on Dynamic Graph with Spatio-Temporal Disentanglement | 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 DGSTD: Learning on Dynamic Graph with Spatio-Temporal Disentanglement Peng You, Xiaohu Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4386314/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 Dynamic graphs serve as abstractions of real-world dynamic networks. They represent a concrete and profound restoration of many scenarios in the real-world. For instance, various types of terminal intelligent agents in social networks, recommendation systems, and biological networks facilitate collaborative work within specific group topologies. Despite recent advancements in research on representation learning for dynamic graphs, the factorized representation of features across different dimensions and potential causality have not been adequately considered or explicitly modeled to capture dynamic patterns. The existing literature predominantly relies on manual extraction of temporal and spatial features, which fails to adequately capture the underlying causal relationships. In this study, we propose a novel Dynamic Graph with Spatio-Temporal Disentanglement (DGSTD) that effectively disentangles the spatio-temporal features of the dynamic network within our model. The proposed method sample and sparsely encode the node attribute features under time constraints to find out meaningful structures and patterns for representing graph features, effectively capturing potential spatio-temporal factorized representation. We further used a combination of loss functions to optimize the model. Our approach exhibits distinct advantages in both transductive and inductive settings across four authentic datasets. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Dynamic graphs Disentanglement Factorized representation 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. 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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-4386314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304664231,"identity":"8518b863-e0ae-4e85-9775-79d6267376d7","order_by":0,"name":"Peng You","email":"","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"You","suffix":""},{"id":304664232,"identity":"b921446c-1cc8-4178-aa2a-91b22309d04e","order_by":1,"name":"Xiaohu Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACCRBhAEYgYMPDz99AkpaENBnJGQeI0cIA13LYxqAhAb8O+dnNDx/zFNjJmUskP5O6+eM8jwHDAcYPH3Nwa2Gcc8zYcIZBsrHljDQz6ZyE2zzmzA3MkjO34dbCLJFgJvHBgDlxw40cNrAWy4YDbMy8eLSwSaR/k0gwqIdpOcdjcCABvxYeiRyQLYdhWg4Q1iIhkVMM9MtxY4Mzz4ytc9KSeSRnHGzG6xf5GekbH/P8qZYzOJ788HaOjZ09P3/zwQ8f8WjBBhgbSFM/CkbBKBgFowADAACjEEvyLAYqjgAAAABJRU5ErkJggg==","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiaohu","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-05-08 03:55:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4386314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4386314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64652926,"identity":"44f340e5-ae22-4f10-90bd-b3cff56f41a6","added_by":"auto","created_at":"2024-09-17 06:03:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677974,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicGraphwithSpatioTemporalDisentanglementSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4386314/v1_covered_05ebe2ff-793a-4bb2-bad1-a42d0c8844f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DGSTD: Learning on Dynamic Graph with Spatio-Temporal Disentanglement","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Dynamic graphs, Disentanglement, Factorized representation","lastPublishedDoi":"10.21203/rs.3.rs-4386314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4386314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDynamic graphs serve as abstractions of real-world dynamic networks. 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