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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)

Paper ID
h4bc2e3d6f24a3179
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,421 | 29.94%
DOI
10.1145/3802023

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Authors

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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
589 Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis 2013 VLDB 0.00015905948
2,042 Efficient Algorithms for Densest Subgraph Discovery 2019 VLDB 9.1416822e-05
5,450 Accurate and Fast Approximate Graph Pattern Mining at Scale 2025 VLDB 6.1247776e-05
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