ResCLG: Improving Recommendation via Contrastive Alignment and Residual Propagation in Graph Networks

ResCLG: Improving Recommendation via Contrastive Alignment and Residual Propagation in Graph Networks | 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 ResCLG: Improving Recommendation via Contrastive Alignment and Residual Propagation in Graph Networks Xingyao Yang, Di Xu, Zulian Zhang, Jiong Yu, Xinsheng Dong, Xinyu Xiong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8309461/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract In the face of the challenges of data sparsity and the difficulty in deeply exploring user-item interaction relationships within recommender systems, this research puts forward a graph-neural-network-based recommendation model named ResCLG, which combines contrastive learning and residual connection. The model deploys a contrastive learning module to enhance the alignment quality of user-item representations. Meanwhile, residual connections are employed to mitigate the over-smoothing issue implications. in deep graph convolution, thereby boosting the model’s representational capacity. The experiments carried out on three benchmark datasets demonstrate that ResCLG outperforms mainstream baseline models like LightGCN and SGL in terms of Recall and NDCG. Specifically, on the Amazon - Book dataset, its performance witnesses an improvement of over 22%. Significantly, ResCLG demonstrates excellent performance When the temperature parameter τ in contrastive learning is set to a relatively low value. Moreover, its performance decline is far less than that of SGL. This implies that the latent representations generated by ResCLG are of superior quality, less susceptible to noise, and can more effectively harness strict contrast signals. This study presents a novel avenue for the construction of an efficient and robust graph recommendation model, carrying substantial theoretical and practical implications. Recommender System Data Sparsity Graph Convolutional Network Contrastive Learning Residual Connection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 11 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviewers agreed at journal 28 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers agreed at journal 26 Dec, 2025 Reviewers invited by journal 26 Dec, 2025 Editor assigned by journal 26 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 08 Dec, 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. 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-8309461","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566414898,"identity":"a001d4c8-fe73-4fee-b29f-edc81be3d83e","order_by":0,"name":"Xingyao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYPACGwbGBiDFQ4KWNNK1HIZQRGkxOH728GvetvOJzTMSGB+8bWOQNyeo5UxemuXMttvGjDMSmA3ntjEY7mwgpOVAjpnBx7bbckAtbNK8bQwJBgcIaTn/xswgse0cD1AL+2/itNzIMX7wse0A2BZmorRI3nhjxjjjXLIxY8/DZsk55yQMNxDSwnc+x/gzT5ld4sb25IMf3pTZyBO0ReEAA5sEIxsDg2EDODIlCKgHAvkGBuYPDH+ADMJqR8EoGAWjYKQCAB+uQnhnxmZxAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"Xingyao","middleName":"","lastName":"Yang","suffix":""},{"id":566414899,"identity":"98812d37-7aa9-4f61-b29d-70356ae0d9f7","order_by":1,"name":"Di Xu","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Xu","suffix":""},{"id":566414900,"identity":"f269c83d-aee6-4fa2-96bd-b0dfa6b8cbe2","order_by":2,"name":"Zulian Zhang","email":"","orcid":"","institution":"Xinjiang Uygur Autonomous Region Meteorological Service","correspondingAuthor":false,"prefix":"","firstName":"Zulian","middleName":"","lastName":"Zhang","suffix":""},{"id":566414901,"identity":"5acd3be9-5dc4-4a6d-a333-4caa8463c9d7","order_by":3,"name":"Jiong Yu","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jiong","middleName":"","lastName":"Yu","suffix":""},{"id":566414903,"identity":"4f5c446e-faae-460e-9c70-e53ed07929a9","order_by":4,"name":"Xinsheng Dong","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Xinsheng","middleName":"","lastName":"Dong","suffix":""},{"id":566414904,"identity":"647a3f3e-444f-4515-ba79-024d8d30b2f5","order_by":5,"name":"Xinyu Xiong","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2025-12-08 15:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8309461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8309461/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99154405,"identity":"064f4108-6526-416b-be2b-f9d343878f02","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":720007,"visible":true,"origin":"","legend":"","description":"","filename":"ResCLG.docx","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/b37964fca44574ac99e88984.docx"},{"id":99154403,"identity":"d00089a4-c205-4a97-abb7-9bf9968682e1","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"json","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7219,"visible":true,"origin":"","legend":"","description":"","filename":"a3ed86634c2d478baa20709b9a6f30d7.json","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/bd1e1caa42470ae984c3ebce.json"},{"id":99317675,"identity":"986abf10-181c-42c2-abfa-b668d7be4280","added_by":"auto","created_at":"2025-12-31 16:30:34","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148771,"visible":true,"origin":"","legend":"","description":"","filename":"a3ed86634c2d478baa20709b9a6f30d71enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/36bc1f898487b4a937918c85.xml"},{"id":99315981,"identity":"3aa60f4b-58db-4195-9fb3-3b3a6ad69d3c","added_by":"auto","created_at":"2025-12-31 16:27:32","extension":"eps","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30850,"visible":true,"origin":"","legend":"","description":"","filename":"amazonres.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/293b1004db8e5e1269eabc26.eps"},{"id":99154406,"identity":"5b654dbe-a45d-4bf3-816b-2efd62f360f2","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165821,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/bb7a4c05e300d2b96be88505.png"},{"id":99317665,"identity":"83f61a9d-6458-4cc3-b086-b1bedf7bbecb","added_by":"auto","created_at":"2025-12-31 16:30:33","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108996,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/62233baf429cb9b83d7b9f32.png"},{"id":99154427,"identity":"59e08e35-dcba-43ba-bd86-5eb0ac487e1b","added_by":"auto","created_at":"2025-12-29 11:18:21","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134859,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/ab451cb6416ae8ed6d44add0.jpeg"},{"id":99154416,"identity":"b9e2085a-cf16-4b05-9a68-798796198108","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":667545,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/2a0bb6f8876785b685b57934.jpeg"},{"id":99154409,"identity":"a196522d-3569-4cd9-942b-60e4429b8e93","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169603,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/78620aebba0105868183d58b.jpeg"},{"id":99315987,"identity":"1568591f-86ba-4b81-8f69-2be39fb53469","added_by":"auto","created_at":"2025-12-31 16:27:33","extension":"eps","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28719,"visible":true,"origin":"","legend":"","description":"","filename":"gowallares.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/74c9cca61d1ec41346d09421.eps"},{"id":99154407,"identity":"22c8dee6-5e91-4b88-95e2-6287ed59ca02","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"eps","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33017,"visible":true,"origin":"","legend":"","description":"","filename":"lambdaamazon.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/acf30d1817abc6e81f484700.eps"},{"id":99154426,"identity":"2df75eca-fbca-44e3-badf-92e5d5a24e2f","added_by":"auto","created_at":"2025-12-29 11:18:19","extension":"eps","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31866,"visible":true,"origin":"","legend":"","description":"","filename":"lambdagowalla.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/87278ec8b216a4804c984045.eps"},{"id":99315558,"identity":"be81fd03-ef1d-424d-bf69-887046240957","added_by":"auto","created_at":"2025-12-31 16:27:04","extension":"eps","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32388,"visible":true,"origin":"","legend":"","description":"","filename":"lambdayelp2018.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/c30489f481131de558718308.eps"},{"id":99315586,"identity":"81ee40ba-68b4-441e-b847-313d6809ecff","added_by":"auto","created_at":"2025-12-31 16:27:05","extension":"eps","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44935,"visible":true,"origin":"","legend":"","description":"","filename":"tauanalysisamazon.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/3fcd7c9aafa262743428ad75.eps"},{"id":99154424,"identity":"ec29e3eb-3266-4a52-8930-183cf65b3dd6","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"eps","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43147,"visible":true,"origin":"","legend":"","description":"","filename":"tauanalysisgowalla.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/a87c2fb0d9ce04a2db98d579.eps"},{"id":99154418,"identity":"c0b1b620-a6af-4f7f-a955-94778ccbbba9","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"eps","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45429,"visible":true,"origin":"","legend":"","description":"","filename":"tauanalysisyelp2018.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/1d456811fad23b49b5eed5d0.eps"},{"id":99154423,"identity":"8dfd3549-1c03-4818-8e4f-ce335248db22","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"eps","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28008,"visible":true,"origin":"","legend":"","description":"","filename":"yelp2018res.eps","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/4205b6bf086e00df96960c5f.eps"},{"id":99315745,"identity":"f342ee5b-3a76-4d23-94ff-e3d1f7066c37","added_by":"auto","created_at":"2025-12-31 16:27:19","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42948,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/50460a6970c6d605ec6d8ddb.png"},{"id":99154414,"identity":"3818a049-67c2-47a0-b25f-296acb282f11","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28094,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/fb5c0f270d629d4d7b7318d5.png"},{"id":99154412,"identity":"3897c535-3b16-48de-8d19-c7aad7b0943a","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21912,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/5d7836da79f01f17d2c58d89.png"},{"id":99154420,"identity":"cdba853c-8454-4f03-8987-b60799b608f4","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98355,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/098ed6fcfa0e59487b75554a.png"},{"id":99154419,"identity":"1f5bbd27-45f0-4642-a20d-39e8d332ac13","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32211,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/64533ba91296adfed085790d.png"},{"id":99317526,"identity":"be638b33-c3d4-4f53-833a-6ac2c34c2b6e","added_by":"auto","created_at":"2025-12-31 16:30:19","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148236,"visible":true,"origin":"","legend":"","description":"","filename":"a3ed86634c2d478baa20709b9a6f30d71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/8a86a3ab393af1b0ad75a97f.xml"},{"id":99154422,"identity":"a7f14d03-0e6e-4873-b374-9823b0d01267","added_by":"auto","created_at":"2025-12-29 11:18:18","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169760,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1/1c870ef9ae698d6daa85ccec.html"},{"id":99323660,"identity":"279f4f69-24f0-4290-a241-b838a072a5bb","added_by":"auto","created_at":"2025-12-31 16:45:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":728687,"visible":true,"origin":"","legend":"","description":"","filename":"ResCLG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309461/v1_covered_ab5389e9-3b60-46a1-acba-4723e3e6f1c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ResCLG: Improving Recommendation via Contrastive Alignment and Residual Propagation in Graph Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recommender System, Data Sparsity, Graph Convolutional Network, Contrastive Learning, Residual Connection","lastPublishedDoi":"10.21203/rs.3.rs-8309461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8309461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the face of the challenges of data sparsity and the difficulty in deeply exploring user-item interaction relationships within recommender systems, this research puts forward a graph-neural-network-based recommendation model named ResCLG, which combines contrastive learning and residual connection. The model deploys a contrastive learning module to enhance the alignment quality of user-item representations. Meanwhile, residual connections are employed to mitigate the over-smoothing issue implications. in deep graph convolution, thereby boosting the model’s representational capacity. The experiments carried out on three benchmark datasets demonstrate that ResCLG outperforms mainstream baseline models like LightGCN and SGL in terms of Recall and NDCG. Specifically, on the Amazon - Book dataset, its performance witnesses an improvement of over 22%. Significantly, ResCLG demonstrates excellent performance When the temperature parameter τ in contrastive learning is set to a relatively low value. Moreover, its performance decline is far less than that of SGL. This implies that the latent representations generated by ResCLG are of superior quality, less susceptible to noise, and can more effectively harness strict contrast signals. This study presents a novel avenue for the construction of an efficient and robust graph recommendation model, carrying substantial theoretical and practical implications.","manuscriptTitle":"ResCLG: Improving Recommendation via Contrastive Alignment and Residual Propagation in Graph Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 11:18:13","doi":"10.21203/rs.3.rs-8309461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-17T19:14:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T03:27:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T06:41:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T00:00:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221430011060436093875427485572631615892","date":"2025-12-28T14:36:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324173626507133788161228064745264639230","date":"2025-12-27T07:56:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159654570690094270612076831873590659827","date":"2025-12-26T13:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-26T12:31:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T12:31:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T10:49:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cluster Computing","date":"2025-12-08T15:33:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"13d1ed00-2ac7-4700-81f0-d0ae8ff3f0b5","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-17T19:14:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T19:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 11:18:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8309461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8309461","identity":"rs-8309461","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}