Efficient Maximum k-Defective Clique Computation with Improved Time Complexity
Summary: Introduces kDC, a framework for exact maximum k-defective clique with sub-2^n time, via non-fully-adjacent-first branching, excess-removal, and high-degree reductions. Separates worst-case techniques from practical optimizations; adds a tighter upper bound, two reductions, and fast initial solution; benchmarks on 290 graphs show kDC vastly outperforms KDBB. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lijun Chang (University of Sydney)
BibTeX Citation
@inproceedings{chang_sigmod23,
title = {{Efficient Maximum k-Defective Clique Computation with Improved Time Complexity}},
author = {Chang, Lijun},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3617313},
url = {https://dl.acm.org/doi/10.1145/3617313},
year = {2023}
}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 96 | Truss Decomposition in Massive Networks | 2012 | VLDB | 0.00034261728 |
| 978 | Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification | 2012 | VLDB | 0.00012699423 |
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