Maximum Biclique Search at Billion Scale
Summary: Divides max-biclique search into O(log n) subproblems with restricted regions; prunes by one-hop/two-hop neighbors while preserving the optimal constrained biclique. Demonstrates billion-scale graphs and a 50% recall gain on Alibaba fraud detection. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bingqing Lyu (Alibaba)
- 2. Lu Qin (University of Technology Sydney)
- 3. Xuemin Lin (University of New South Wales)
- 4. Ying Zhang (University of Technology Sydney)
- 5. Zhengping Qian (Alibaba)
- 6. Jingren Zhou (Alibaba)
BibTeX Citation
@article{lyu_vldb20,
title = {{Maximum Biclique Search at Billion Scale}},
author = {Lyu, Bingqing and Qin, Lu and Lin, Xuemin and Zhang, Ying and Qian, Zhengping and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {9},
pages = {1359--1372},
doi = {10.14778/3397230.3397234},
url = {https://doi.org/10.14778/3397230.3397234},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
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Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,509 | Finding the Maximum Clique in Massive Graphs | 2017 | VLDB | 0.00010539891 |
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