In-depth Analysis of Densest Subgraph Discovery in a Unified Framework
Summary: Presents a unified high-level framework that subsumes exact and approximation Densest Subgraph Discovery algorithms and performs a systematic, large-scale empirical comparison across small to billion-node graphs. Identifies new algorithmic variants for undirected graphs—by combining existing techniques—to achieve up to 10× speedups with identical accuracy guarantees and surfaces practical and theoretical research directions. (summarized by gpt-5-mini on Feb 09 2026)
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No non-self incoming citations found for this paper in this database.
Authors
- 1. Yingli Zhou (Chinese University of Hong Kong)
- 2. Qingshuo Guo (Chinese University of Hong Kong)
- 3. Yi Yang (Chinese University of Hong Kong)
- 4. Yixiang Fang (Chinese University of Hong Kong)
- 5. Chenhao Ma (Chinese University of Hong Kong)
- 6. Laks V.S. Lakshmanan (University of British Columbia)
BibTeX Citation
@article{zhou_vldb25,
title = {{In-depth Analysis of Densest Subgraph Discovery in a Unified Framework}},
author = {Zhou, Yingli and Guo, Qingshuo and Yang, Yi and Fang, Yixiang and Ma, Chenhao and Lakshmanan, Laks V.S.},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {4},
pages = {1131--1144},
doi = {10.14778/3717755.3717771},
url = {https://doi.org/10.14778/3717755.3717771},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,454 | Efficient Anchored Densest Subgraph Discovery: Improved Time Complexity and Practical Performance | 2026 | SIGMOD | 4.9769913e-05 |
| 10,573 | Efficient and Scalable Directed Densest Subgraph Discovery | 2026 | SIGMOD | 4.9769913e-05 |
| 10,818 | Efficient Locally h-Clique Densest Subgraph Discovery via Divide-and-Conquer | 2026 | VLDB | 4.9769913e-05 |
| 11,329 | Efficient k-Clique Densest Subgraph Discovery: Towards Bridging Practice and Theory | 2025 | VLDB | 4.9769913e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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