Efficient Size-Bounded Community Search, Revisited: Frameworks for Practical Improvements
Summary: Revisits Size-bounded Community Search (connected subgraph containing query q, size in [l,h], maximize min-degree) and introduces a branch-and-bound that efficiently isolates structurally compact candidate subsets. Three frameworks plus a heuristic, two reduction rules and query decomposition yield ~100× speedups and denser communities on 12 large graphs vs prior exact methods. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Yang Liu (Harbin Engineering University)
- 2. Hejiao Huang (Harbin Engineering University)
- 3. Kaiqiang Yu (Nanjing University)
- 4. Shengxin Liu (Harbin Engineering University)
- 5. Cheng Long (Nanyang Technological University)
- 6. Zhaoquan Gu (Harbin Engineering University; Peng Cheng Laboratory)
BibTeX Citation
@inproceedings{liu_sigmod26,
title = {{Efficient Size-Bounded Community Search, Revisited: Frameworks for Practical Improvements}},
author = {Liu, Yang and Huang, Hejiao and Yu, Kaiqiang and Liu, Shengxin and Long, Cheng and Gu, Zhaoquan},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769789},
url = {https://dl.acm.org/doi/10.1145/3769789},
year = {2026}
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