A Personalized Mining Algorithm for Grassroots Network Data Based on Deep Learning

A Personalized Mining Algorithm for Grassroots Network Data Based on Deep Learning | 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 A Personalized Mining Algorithm for Grassroots Network Data Based on Deep Learning Connor Phillips, Ank Agarwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8559081/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 enhance the accuracy and efficiency of mining grassroots network data and to better support practical applications, this study proposes a personalized mining algorithm for grassroots network data based on deep learning. A multi-module neural network architecture is designed to process, filter, and transform raw grassroots network data. Through data preprocessing and hierarchical refinement, the algorithm generates high-precision, structured datasets suitable for personalized mining tasks. A five-layer neural network-comprising an input layer, convolutional input layer, hidden layer, convolutional output layer, and prediction output layer-is constructed to support an integrated training and testing workflow. A redundancy-elimination rule is introduced to prune unnecessary neural network parameters, followed by a maximum weight extraction rule to guide the personalized mining of grassroots network data. Experimental results demonstrate that the proposed algorithm achieves high convergence speed during training and offers superior performance in testing accuracy, enabling precise and reliable mining of high-dimensional and heavily interconnected data. This work lays a technical foundation for the effective utilization and intelligent analysis of grassroots network data. deep learning neural networks grassroots network data data mining pruning rules feature selection Full Text Additional Declarations The authors declare no competing interests. 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. 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