A Hybrid Framework for Community Detection Integrating Clauset Newman Moore (CNM), Graph Aggregation, and Cuckoo Search

A Hybrid Framework for Community Detection Integrating Clauset Newman Moore (CNM), Graph Aggregation, and Cuckoo Search | 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 Article A Hybrid Framework for Community Detection Integrating Clauset Newman Moore (CNM), Graph Aggregation, and Cuckoo Search Mukesh Sakle Mukesh, Deepali Piple Deepali, Shaligram Prajapat Shaligram This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9040605/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The prime focus of community detection is to reveal groups of nodes that are densely connected within the network (internally) and sparsely connected to the rest of the network. Although, challenge remains to identify meaningful community structures in large and complex networks due to hierarchical organization, heterogeneous connectivity, and resolution-limit effects. The greedy modularity-based methods such as the Clauset–Newman–Moore (CNM) algorithm is computationally efficient but often suffer from immutable merge decisions and poor performance at multiple structural scales. This paper proposes a hybrid multi-scale community detection framework that combines CNM-based hierarchical aggregation with a Cuckoo Search–based refinement strategy to address these limitations. At first, CNM is applied to obtain an initial fine-scale partition, then community-based graph aggregation is followed to reduce the search space and mitigates resolution bias. At final step, a Cuckoo Search–based optimization mechanism employed Levy-flight-inspired node perturbations is used to escape local optima and refine community assignments by maximizing modularity. Experimental analysis conducted on multiple real-world and stimulate network, like Karate, Dolphin, Football, Facebook, Polbooks, Les-mis (Les Misérables), Jazz, and LFR benchmarks. The proposed hybrid approach indicates consistently improvement in modularity and clustering accuracy over standard CNM and other baseline methods. Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Editor invited by journal 01 Apr, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 18 Mar, 2026 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-9040605","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615857462,"identity":"32a97bff-427a-4a37-bde8-d0a97a0f60d5","order_by":0,"name":"Mukesh Sakle Mukesh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACZoYHDDZAmrEBxidCSwJDGkhLYwMpWg4zIFlDAOj2H35skFBx3l7e/3D7wx8MdvIM7LwH8Goxu5FmnJBw5nbixhuJjc08DMmGDcx8CQS0MBgfSGy7nWA4g7GxGejKBAZmHgP8Ws4f/3wg8d85e8P+g42NPxjqidByIMc4IbHhAON8hsTGBh6Gw0RouZFTbJBwLDlxg0Ri42weg+OGbUQ4bLPEhxo7e/n+4w8+/qiolufnP4NfCxwYHACTDAxsxKkHAvkGopWOglEwCkbBSAMA2nJEUmW5xjoAAAAASUVORK5CYII=","orcid":"","institution":"Shri G.S. 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Although, challenge remains to identify meaningful community structures in large and complex networks due to hierarchical organization, heterogeneous connectivity, and resolution-limit effects. The greedy modularity-based methods such as the Clauset\u0026ndash;Newman\u0026ndash;Moore (CNM) algorithm is computationally efficient but often suffer from immutable merge decisions and poor performance at multiple structural scales. This paper proposes a hybrid multi-scale community detection framework that combines CNM-based hierarchical aggregation with a Cuckoo Search\u0026ndash;based refinement strategy to address these limitations. At first, CNM is applied to obtain an initial fine-scale partition, then community-based graph aggregation is followed to reduce the search space and mitigates resolution bias. At final step, a Cuckoo Search\u0026ndash;based optimization mechanism employed Levy-flight-inspired node perturbations is used to escape local optima and refine community assignments by maximizing modularity. Experimental analysis conducted on multiple real-world and stimulate network, like Karate, Dolphin, Football, Facebook, Polbooks, Les-mis (Les Mis\u0026eacute;rables), Jazz, and LFR benchmarks. 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