DATIS: Data Augmentation for Trust Intensity Prediction in Incomplete Signed Networks

DATIS: Data Augmentation for Trust Intensity Prediction in Incomplete Signed 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 DATIS: Data Augmentation for Trust Intensity Prediction in Incomplete Signed Networks Shiva Shadrooh, Kjetil Nørvåg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4481271/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted 9 You are reading this latest preprint version Abstract Prediction of trust and distrust in nodes in signed network analysis is an important task with diverse applications. However, the presence of imbalanced and incomplete rankings in signed networks makes prediction of node-level trust values using machine learning (ML) methods a very challenging task. To overcome these challenges, we introduce \method, an innovative approach employing generative adversarial networks (GANs) for data augmentation in node-level trust prediction tasks in signed networks. \method addresses imbalances in both sign and value of rankings, handling missing rankings by training on nodes' local and global network features without explicit information on edge rankings assigned to nodes. Unlike existing methods, we consider the trust prediction problem as a regression task to imply the strength of trust a node gained in a network. Our experimental evaluation shows that \methodcan significantly improve the accuracy of node-level trust intensity prediction on real-world datasets. Trust and Distrust Prediction Signed Networks Generative Adversarial Networks Node Ranking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted Editorial decision: Revision requested 31 Aug, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 21 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers invited by journal 24 Jun, 2024 Editor assigned by journal 28 May, 2024 Submission checks completed at journal 27 May, 2024 First submitted to journal 26 May, 2024 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. 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