Bound-Tightened Densest Subgraph Discovery on GPU
Summary: GPU-accelerated exact densest k-subgraph discovery, combining warp-level k-clique enumeration, clique-core/edge pruning, and component decomposition. Parallel push-relabel max-flow with tight density bounds delivers substantial speedups over CPU baselines while preserving optimality. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Wajid Manzoor (Rowan University)
- 2. Ke Fan (Temple University)
- 3. Muhammad Shaheer (Rowan University)
- 4. Guimu Guo (Rowan University)
BibTeX Citation
@inproceedings{manzoor_sigmod26,
title = {{Bound-Tightened Densest Subgraph Discovery on GPU}},
author = {Manzoor, Wajid and Fan, Ke and Shaheer, Muhammad and Guo, Guimu},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802023},
url = {https://dl.acm.org/doi/10.1145/3802023},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 590 | Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis | 2013 | VLDB | 0.0001605572 |
| 2,024 | Efficient Algorithms for Densest Subgraph Discovery | 2019 | VLDB | 9.2907829e-05 |
| 5,506 | Accurate and Fast Approximate Graph Pattern Mining at Scale | 2025 | VLDB | 6.1925892e-05 |
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