MPRW-HGNN: A Meta-path Random Walk based Heterogeneous Graph Neural Network

MPRW-HGNN: A Meta-path Random Walk based Heterogeneous Graph 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 MPRW-HGNN: A Meta-path Random Walk based Heterogeneous Graph Neural Network Hongjuan Pei, Qingyuan Liu, Jian Xue, KuiJie Zhang, Kun Dong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7782563/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In recent years, graph neural networks have been widely used in multimedia information retrieval and other graph data processing tasks due to their powerful feature learning capabilities. However, existing graph neural network models typically handle only homogeneous graph data with simple structures and single semantics effectively. However, when faced with heterogeneous multimedia data characterized by complex interactions and rich semantics, the performance of traditional models degrades significantly. To address this challenge, this paper proposes a heterogeneous graph neural network algorithm based on meta-path random walk (MPRW-HGNN). First, we design a module at the meta-path instance level that generates meta-path instances and their structural representations via random walks. Then, a soft-attention mechanism is employed to fuse information from multiple meta-paths, better capturing the fine-grained semantic structures around nodes. Subsequently, we utilize a self-attention mechanism to explore semantic correlations and differences between multiple paths, enabling adaptive weighted fusion of multiple path information to generate robust node features for heterogeneous graph data. Through extensive experiments on information retrieval tasks using the IMDB movie dataset and DBLP academic paper dataset, we demonstrate the significant advantages of our proposed algorithm in handling multimodal data and improving retrieval accuracy. heterogeneous graph neural networks meta-path random walk soft attention mechanisms multimedia information retrieval Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 25 Oct, 2025 Submission checks completed at journal 25 Oct, 2025 First submitted to journal 04 Oct, 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. 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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-7782563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540170927,"identity":"7b6b557b-dc06-47a1-903a-a385b5da5037","order_by":0,"name":"Hongjuan Pei","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Pei","suffix":""},{"id":540170928,"identity":"bcb921cd-f3b7-4277-bf78-51390f569c2a","order_by":1,"name":"Qingyuan Liu","email":"","orcid":"","institution":"University of Chinese Academy of 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