Evaluating Regression Models for Link Prediction using Single-Valued Neutrosophic Sets

Evaluating Regression Models for Link Prediction using Single-Valued Neutrosophic Sets | 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 Evaluating Regression Models for Link Prediction using Single-Valued Neutrosophic Sets Abhijay Sai Paladugu, Ilanthenral Kandasamy, Rishabh Sunil Rathi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014034/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Link prediction involves predicting an absent or possible connection between two nodes in a network. It aids in working with incomplete data, improving recommendations, detecting fraud, and understanding network dynamics. This study proposes a new framework that utilizes Single-Valued Neutrosophic Sets (SVNSs) and cosine similarity to perform link prediction. Based on user ratings, it is implemented on the MovieLens, MovieTweetings, and Anime Recommendation datasets. Twenty regression models were trained for link prediction based on the SVNS lists created for each pair of users. The application of cosine similarity revealed significant insights into the relationship between user ratings. The addition of SVNS enhances the model's performance manifold. The performance of twenty regression models was compared against several evaluation metrics and visualized using residual plots and prediction error plots. The proposed methodology using Extra Trees regressor and Extreme Gradient Boosting regressor models provided the best R-squared scores of 0.9100, 0.9380, and 0.7580 for the MovieLens, MovieTweetings, and Anime Recommendation datasets. The proposed models showed better results than baseline models and also promising performance in link prediction. These findings about the proposed methodology suggest that it can be successful for similar datasets within the same domain. Neutrosophy Link Prediction Single Valued Neutrosophic Sets (SVNS) Cosine Similarity Regression Models Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 18 Apr, 2025 Editor assigned by journal 12 Apr, 2025 First submitted to journal 11 Apr, 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-5014034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444727465,"identity":"e1d3397e-0004-410e-a59b-a96876703dbb","order_by":0,"name":"Abhijay Sai Paladugu","email":"","orcid":"","institution":"VIT: Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Abhijay","middleName":"Sai","lastName":"Paladugu","suffix":""},{"id":444727466,"identity":"56f0196e-d63a-4eb3-a0a9-7ccf0ecb9928","order_by":1,"name":"Ilanthenral Kandasamy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACNnYgwdgAYjI3MDZUMPBAxA3waGEGEgfBWhiBWs4w8PAQ0sKAoqWxjQFmDW7Ax8z87PHHHTZ55u0HGxhnzjssY8/A/PADQ8EdPA5jMzc4eCatWOZMYgPjxm2HgQ5jM5ZgMHiGzy9mEgfbDifOYABqebgtDeQXM6BfDuPRwv4NqOV/4gz+h0Atc0Ba2L8R0MIDsuVA4gwJkMMabIBaeAjZwlMmcbYtGajlYcPBGceAWg7zFEsk4NEi396+TaKyzQ7osOSDD3tqJOzZ29s3fvjwB7cWFHAATIJiKoE4DaNgFIyCUTAKcAAAQ4FM1XWkg4YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4826-9466","institution":"VIT University","correspondingAuthor":true,"prefix":"","firstName":"Ilanthenral","middleName":"","lastName":"Kandasamy","suffix":""},{"id":444727467,"identity":"7f792920-199b-4a3b-8e92-5664621fffea","order_by":2,"name":"Rishabh Sunil Rathi","email":"","orcid":"","institution":"VIT: Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rishabh","middleName":"Sunil","lastName":"Rathi","suffix":""},{"id":444727469,"identity":"19b7bafa-1142-4b34-b855-436016c207d4","order_by":3,"name":"Vasantha W B","email":"","orcid":"","institution":"VIT: Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Vasantha","middleName":"W","lastName":"B","suffix":""},{"id":444727471,"identity":"cbe85352-b34a-4b76-bba6-5e94eb592bf8","order_by":4,"name":"Florentin Smarandache","email":"","orcid":"","institution":"University of New Mexico - Albuquerque: The University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Florentin","middleName":"","lastName":"Smarandache","suffix":""}],"badges":[],"createdAt":"2024-09-01 18:00:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5014034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5014034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81240108,"identity":"3ff280de-7dac-470f-873c-f8f4e3c41a9d","added_by":"auto","created_at":"2025-04-23 21:33:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1424962,"visible":true,"origin":"","legend":"","description":"","filename":"EvaluatingRegressionModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5014034/v1_covered_9c8f7ed8-0d51-48ba-85c0-6e26cd17acc8.pdf"}],"financialInterests":"","formattedTitle":"Evaluating Regression Models for Link Prediction using Single-Valued Neutrosophic Sets","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neutrosophy, Link Prediction, Single Valued Neutrosophic Sets (SVNS), Cosine Similarity, Regression Models","lastPublishedDoi":"10.21203/rs.3.rs-5014034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5014034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLink prediction involves predicting an absent or possible connection between two nodes in a network. 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