Efficiently Computing k-Edge Connected Components via Graph Decomposition
Summary: Novel graph-decomposition paradigm for computing k-edge connected components via iterative drilling-down; iterations are bounded by the nesting depth of components (h). Threshold-based per-iteration decomposition runs in O(l|E|) (l small), yielding O(h l|E|) total and major speedups over O(|V|^2|E|+|V|^3 log|V|); evaluated on large real and synthetic graphs. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Lijun Chang (East China Normal University; University of New South Wales)
- 2. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 3. Lu Qin (Chinese University of Hong Kong)
- 4. Xuemin Lin (East China Normal University; University of New South Wales)
- 5. Chengfei Liu (Swinburne University of Technology)
- 6. Weifa Liang (Australian National University)
BibTeX Citation
@inproceedings{chang_sigmod13,
title = {{Efficiently Computing k-Edge Connected Components via Graph Decomposition}},
author = {Chang, Lijun and Yu, Jeffrey Xu and Qin, Lu and Lin, Xuemin and Liu, Chengfei and Liang, Weifa},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465323},
url = {https://dl.acm.org/doi/10.1145/2463676.2465323},
year = {2013}
}
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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 |
|---|---|---|---|---|
| 262 | On Triangulation-based Dense Neighborhood Graph Discovery | 2011 | VLDB | 0.00023084332 |
| 674 | Finding Maximal Cliques in Massive Networks by H*-graph | 2010 | SIGMOD | 0.00015081844 |
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