Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
This paper proposes SCISE, a framework that addresses structural isolation in graph clustering by combining community-aware sampling with constrained structural entropy. It introduces three components: SECC for optimizing structural information, CSampE for preserving global topology during batch training, and StructCL for learning high-order structural representations. Experiments on six benchmarks show state-of-the-art performance.
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[Submitted on 6 Jul 2026]
Title:Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
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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.
Comments: Accepted to the Proceedings of the VLDB Endowment (VLDB 2026). 18 pages, 15 figures, 15 tables
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
Cite as: arXiv:2607.05469 [cs.LG]
(or arXiv:2607.05469v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.05469
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jingyun Zhang [view email] [v1] Mon, 6 Jul 2026 07:55:53 UTC (4,071 KB)
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