Dynamic Community Detection in Mobile Communication Networks Using Deep Representation Learning and Gaussian Mixture Models

Dynamic Community Detection in Mobile Communication Networks Using Deep Representation Learning and Gaussian Mixture Models | 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 Dynamic Community Detection in Mobile Communication Networks Using Deep Representation Learning and Gaussian Mixture Models Xumin Zhao, Fenghua Liu, Lin Zhu, Fangyuan Liu, Li Shen, Mengli Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5592658/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 A mobile communication network is a complex system with millions of interconnected devices, each acting as a node and their communication forming edges. Securing such large-scale networks is a challenging and crucial area of research. While dynamic network structure detection is effective, existing methods often overlook the network topology and local disturbances. Therefore, effectively identifying community structures within the communication network and addressing multimedia security issues remains a significant challenge. This paper presents a dynamic community detection model (DCDAL) based on graph self-coding and a Gaussian mixture model integrated with representation learning to address these issues. The model's performance is evaluated using key network metrics such as NMI and modularization in complex communication networks. Results indicate that the DCDAL model outperforms the comparison model regarding NMI and other indicators, particularly in large-scale mobile communication datasets. The model demonstrates robust performance across multiple evaluation metrics. Dynamic community detection Representation learning Graph self-encoding Gaussian mixture model Mobile communication networks Network security 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-5592658","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441678658,"identity":"5e0a2177-09ef-4c38-a35d-ee4b3821e911","order_by":0,"name":"Xumin Zhao","email":"","orcid":"","institution":"Zhejiang Yuexiu University","correspondingAuthor":false,"prefix":"","firstName":"Xumin","middleName":"","lastName":"Zhao","suffix":""},{"id":441678659,"identity":"2090208d-8db3-4ee4-9e6c-e6fb11e71007","order_by":1,"name":"Fenghua Liu","email":"","orcid":"","institution":"Huzhou 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