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Dupin: A Parallel Framework for Densest Subgraph Discovery in Fraud Detection on Massive Graphs

Summary: Dupin is a parallel DSD framework for billion-scale fraud graphs, exploiting peeling-based properties for scalable, quality-guaranteed detection with a flexible API. Up to 100x speedups; fraud detection improves 45.3% to 94.5%, density error below 5%. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7280
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,758 | 26.20%
DOI
10.1145/3725287

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BibTeX Citation

@inproceedings{jiang_sigmod25,
        title = {{Dupin: A Parallel Framework for Densest Subgraph Discovery in Fraud Detection on Massive Graphs}},
        author = {Jiang, Jiaxin and Yao, Siyuan and Li, Yuchen and Wang, Qiange and He, Bingsheng and Chen, Min},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725287},
        url = {https://dl.acm.org/doi/10.1145/3725287},
        year = {2025}
}

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