Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Summary: SCISE addresses structural isolation in mini-batch graph contrastive clustering via constrained structural entropy and community-aware sampling. Its StructCL module preserves higher-order topology by refining intra-batch edge weights, yielding stronger large-scale clustering. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Jingyun Zhang (Beihang University)
- 2. Hao Peng (Beihang University; Hangzhou Innovation Institute, Beihang University)
- 3. Jianxin Li (Beihang University)
- 4. Angsheng Li (Beihang University)
- 5. Philip S. Yu (University of Illinois Chicago)
BibTeX Citation
@article{zhang_vldb26,
title = {{Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy}},
author = {Zhang, Jingyun and Peng, Hao and Li, Jianxin and Li, Angsheng and Yu, Philip S.},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3497--3510},
doi = {10.14778/3836663.3836704},
url = {https://doi.org/10.14778/3836663.3836704},
year = {2026}
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