Scaling Up k-Clique Densest Subgraph Detection
Summary: Proposes SCT*-Index, a succinct clique-tree index that compactly stores k-cliques and enables direct retrieval for the k-clique densest subgraph problem. Introduces SCTL and SCTL* with graph reductions and batch processing, plus a sampling-based approximation for billion-scale graphs, delivering up to two orders of magnitude speedups over prior approaches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yizhang He (University of New South Wales)
- 2. Kai Wang (Shanghai Jiao Tong University)
- 3. Wenjie Zhang (University of New South Wales)
- 4. Xuemin Lin (Shanghai Jiao Tong University)
- 5. Ying Zhang (University of Technology Sydney)
BibTeX Citation
@inproceedings{he_sigmod23,
title = {{Scaling Up k-Clique Densest Subgraph Detection}},
author = {He, Yizhang and Wang, Kai and Zhang, Wenjie and Lin, Xuemin and Zhang, Ying},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588923},
url = {https://dl.acm.org/doi/10.1145/3588923},
year = {2023}
}
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