Research on multimodal link prediction method based on Vision Transformer and convolutional neural network

Research on multimodal link prediction method based on Vision Transformer and convolutional neural network | 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 Research on multimodal link prediction method based on Vision Transformer and convolutional neural network Yang Liu, Zehong Ren, Xuemei Liu, Xingzhi Wang, Yize Wang, HuiYu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4489200/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 To address the problems of inadequate feature representation and low generalisation ability of existing link prediction methods. A multimodal link prediction method based on Vision Transformer and convolutional neural network is proposed. Firstly, PHash is employed at the filter gate to filter out irrelevant images. Secondly, picture features are extracted using Vision Transformer model and computed using MRP through forgetting gate. Multi-layer Convolutional Neural Networks are used to fuse spatial location feature information during entity relationship embedding to effectively obtain richer semantic information. Meanwhile, a multi-scale null convolution kernel is used to capture rich explicit interaction features in different scale spaces. Finally, the picture features are fused with entity-relationship features in the fusion gate. The experimental results show that the MRR metrics are improved by 17.3% compared with the DistMult model on the public dataset and 4.1% compared with the TuckER model on the e-commerce dataset. link prediction Vision Transformer multiscale dilated convolution multimodal Neural network 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. 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-4489200","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311428247,"identity":"3972d3c3-edef-4ce8-8444-9c310c66ae47","order_by":0,"name":"Yang 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