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)
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Authors
- 1. Jiaxin Jiang (National University of Singapore)
- 2. Siyuan Yao (National University of Singapore)
- 3. Yuchen Li (Singapore Management University)
- 4. Qiange Wang (National University of Singapore)
- 5. Bingsheng He (National University of Singapore)
- 6. Min Chen (GrabTaxi Holdings)
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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