Edge-GNN: A Constraint-Aware Graph Neural Network Framework for Resource-Efficient Biological Interaction Modeling

Edge-GNN: A Constraint-Aware Graph Neural Network Framework for Resource-Efficient Biological Interaction Modeling | 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 Edge-GNN: A Constraint-Aware Graph Neural Network Framework for Resource-Efficient Biological Interaction Modeling Swapin Vidya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9096630/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 Graph neural networks (GNNs) have become an effective framework for modeling biological interaction networks such as protein–protein interaction graphs and gene regulatory systems. However, many existing GNN approaches are designed primarily for high-performance computing environments and do not explicitly account for computational constraints such as memory usage or inference latency. In this study we introduce Edge-GNN, a constraint-aware training framework for graph neural networks designed to balance predictive performance with computational efficiency. The proposed approach incorporates a multi-objective optimization formulation that jointly minimizes predictive loss together with proxy measures of model complexity and computational latency. The framework is evaluated using several graph neural network architectures including Graph Convolutional Networks, GraphSAGE, and Graph Attention Networks. Experiments conducted on the PROTEINS benchmark dataset and transcriptomic data derived from The Cancer Genome Atlas integrated with protein interaction networks demonstrate that the proposed approach reduces computational cost while maintaining comparable predictive performance. These findings suggest that incorporating computational constraints directly into the training objective can improve the practicality of graph-based learning methods for biological network analysis in resource-constrained environments. Bioinformatics Computational Biology graph neural networks computational biology protein interaction networks resource-efficient machine learning 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-9096630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604583283,"identity":"45b905ff-0094-423f-8655-3b134458c09b","order_by":0,"name":"Swapin Vidya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYLCCBAYbEMXGDCaJ1JIG02JApBYGhsMILQSB/IzkZw8e7jifx89++Nnjgoo/DHzSDfi1GNxIMzdIPHO7WLInzdx4xhmgw2QOENAinWAmkdh2O3HDgRw2ad42oBaJBAIOm53+DajlXOL+82+I1MJwOwdky4HEDRLE2mJw/025QWJbcrHEjWdm0jPOGPMQdljP8W0Pf7bZ5fH3Jz+TLqiQk5OfQchh0OiGK+MhqB5DyygYBaNgFIwCDAAAbMg9a8xFeioAAAAASUVORK5CYII=","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Swapin","middleName":"","lastName":"Vidya","suffix":""}],"badges":[],"createdAt":"2026-03-11 16:26:06","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-9096630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9096630/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781611,"identity":"0457a3dc-952e-47dc-8c9a-d78e023b2879","added_by":"auto","created_at":"2026-03-17 07:56:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311628,"visible":true,"origin":"","legend":"","description":"","filename":"wsjbcb.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9096630/v1_covered_219a06e4-566e-4064-b642-003c991e07f5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEdge-GNN: A Constraint-Aware Graph Neural Network Framework for Resource-Efficient Biological Interaction Modeling\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":"graph neural networks; computational biology; protein interaction networks; resource-efficient machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9096630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9096630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGraph neural networks (GNNs) have become an effective framework for modeling biological interaction networks such as protein–protein interaction graphs and gene regulatory systems. 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