SE-GCL: Structure-Aware Graph Clustering with Entropy Minimization

SE-GCL: Structure-Aware Graph Clustering with Entropy Minimization | 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 SE-GCL: Structure-Aware Graph Clustering with Entropy Minimization Guoteng Xu, Xiudian Zhang, Lingjie Wang, Jianjiang Liu, Hanlin Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9158379/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Unsupervised graph clustering has become an important method for uncovering patterns in the latent community structure of graph nodes; however, existing methods face the challenge of simultaneously capturing both the local structural differences and the global organizational patterns in graph data.At the same time, mainstream depth map clustering approaches stack numerous network layers to capture global features, which generally suffer from the drawbacks of massive parameter counts and computational redundancy. Meanwhile, while traditional structural entropy clustering can quantify topological disorder, it is constrained by the non-differentiability of structural entropy, making it difficult to integrate into end-to-end deep learning frameworks for joint optimization.To address these issues, this paper proposes an unsupervised graph clustering framework—SE-GCL—based on structural entropy and probabilistic optimization. By overcoming the inherent limitation of traditional clustering algorithms that require a predefined number of clusters, SE-GCL achieves simultaneous optimization of adaptive cluster discovery and node cluster assignment.This method consists of a Structural Entropy Guidance (SEO) module and a Clustering Search (CSM) module: The SEO module utilizes a second-order global structural entropy metric to characterize the uncertainty and structural contribution of nodes within the graph structure, This paper designs a parameter-free indirect optimization strategy to address the non-differentiability of structural entropy, simultaneously capturing local inter-node associations and global topological distribution patterns. Combined with the CSM, it performs probabilistic inference and iterative updates of cluster labels, enhancing the stability of complex graph representations; the CSM module introduces a temperature-coefficient-based probabilistic update mechanism to dynamically optimize node cluster assignments, making the clustering process better align with the graph’s intrinsic structural characteristics. Experiments on multiple public graph datasets demonstrate that this method not only adaptively discovers optimal cluster partitions but also improves the efficiency of clustering tasks through its lightweight nature. The SE-GCL model achieves 80%–96% of the performance of state-of-the-art models while using only 12%–55% of the model parameters. 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 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 25 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Editor invited by journal 25 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 23 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. 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-9158379","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610940350,"identity":"156b0dea-0f9e-434f-94c8-2882f55a2ade","order_by":0,"name":"Guoteng Xu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Guoteng","middleName":"","lastName":"Xu","suffix":""},{"id":610940351,"identity":"48061cb4-ff35-47a8-b598-ea6de8325aea","order_by":1,"name":"Xiudian 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Meanwhile, while traditional structural entropy clustering can quantify topological disorder, it is constrained by the non-differentiability of structural entropy, making it difficult to integrate into end-to-end deep learning frameworks for joint optimization.To address these issues, this paper proposes an unsupervised graph clustering framework—SE-GCL—based on structural entropy and probabilistic optimization. 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