{"ID":6023770,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-09T07:52:46.28543944Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05469","arxiv_id":"2607.05469","title":"Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy","abstract":"Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the \"structural isolation\" issue during mini-batch training, making it challenging to capture cohesive community structures that characterize the global topological distribution. To address these challenges, we propose SCISE, a Scalable unsupervised graph Clustering framework that preserves structural Integrity by synergizing community-aware sampling with constrained Structural Entropy. Specifically, we first introduce the Structural Entropy Community Constraint operator (SECC), which optimizes structural information within a constrained solution space to mitigate community fragmentation and enhance partition cohesion. Second, to prevent global information loss during batch training, we design a Community-Aware Sampling Expansion (CSampE) mechanism that incorporates the community context of target nodes into sampling batches, effectively breaking structural barriers and preserving topological integrity. Finally, we devise a Structural Contrastive Learning (StructCL) module that refines edge weights based on intra-batch structural similarity, guiding the encoder to learn representations in a higher-order structural space. Extensive experiments on six mainstream benchmark datasets demonstrate that SCISE significantly outperforms state-of-the-art algorithms, with ablation studies and robustness analyses further validating its effectiveness and reliability for real-world large-scale graphs.","short_abstract":"Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the \"structural isolation\" issue during mini-batch training, making it challenging...","url_abs":"https://arxiv.org/abs/2607.05469","url_pdf":"https://arxiv.org/pdf/2607.05469v1","authors":"[\"Jingyun Zhang\",\"Hao Peng\",\"Jianxin Li\",\"Angsheng Li\",\"Philip S. Yu\"]","published":"2026-07-06T07:55:53Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.SI\"]","methods":"[]","has_code":false}