Efficient and Scalable Directed Densest Subgraph Discovery
Summary: Novel provable graph-reduction that isolates the directed densest subgraph into a much smaller instance, enabling drastic pruning. A gradient-projection approximation with faster convergence and an exact algorithm built on it, yielding up to 100× speedups. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Yingli Zhou (Chinese University of Hong Kong)
- 2. Luocheng Liang (Chinese University of Hong Kong)
- 3. Yixiang Fang (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{zhou_sigmod26,
title = {{Efficient and Scalable Directed Densest Subgraph Discovery}},
author = {Zhou, Yingli and Liang, Luocheng and Fang, Yixiang},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769785},
url = {https://dl.acm.org/doi/10.1145/3769785},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,442 | Efficient Anchored Densest Subgraph Discovery: Improved Time Complexity and Practical Performance | 2026 | SIGMOD | 4.9793485e-05 |
| 10,665 | Order-based Algorithms for Efficient Core Maintenance in Large Bipartite Graphs | 2026 | SIGMOD | 4.9793485e-05 |
| 10,808 | Efficient Locally h-Clique Densest Subgraph Discovery via Divide-and-Conquer | 2026 | VLDB | 4.9793485e-05 |
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