RICH: Real-time Identification of negative Cycles for High-efficiency Arbitrage
Summary: Model arbitrage as k-hop negative-cycle detection and introduce RICH, which employs color-coding plus dynamic programming to probabilistically find negative-weight cycles without exhaustive traversal. Adds encoding and graph-reduction to cut overhead, enabling real-time detection with strong probabilistic guarantees and up to 32.7× speedups on crypto/FX benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Bingqiao Luo (National University of Singapore)
- 2. Jiaxin Jiang (National University of Singapore)
- 3. Yuhang Chen (National University of Singapore)
- 4. Junyi Hou (National University of Singapore)
- 5. Cheng Jun Tey (National University of Singapore)
- 6. Ziyang Qiu (National University of Singapore)
- 7. Bingsheng He (National University of Singapore)
- 8. Spencer Xiao (Tokka Labs)
- 9. Dominic Ong (Tokka Labs)
- 10. Wee Howe Ang (Tokka Labs)
BibTeX Citation
@article{luo_vldb25,
title = {{RICH: Real-time Identification of negative Cycles for High-efficiency Arbitrage}},
author = {Luo, Bingqiao and Jiang, Jiaxin and Chen, Yuhang and Hou, Junyi and Tey, Cheng Jun and Qiu, Ziyang and He, Bingsheng and Xiao, Spencer and Ong, Dominic and Ang, Wee Howe},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4081--4089},
doi = {10.14778/3749646.3749678},
url = {https://doi.org/10.14778/3749646.3749678},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,490 | ORDER: Optimal Routing with Path Indexing in Exchange Graph [Data-Intensive & Data Science Application] | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 937 | Real-time Constrained Cycle Detection in Large Dynamic Graphs | 2018 | VLDB | 0.00012977594 |
| 4,634 | Hop-constrained s-t Simple Path Enumeration: Towards Bridging Theory and Practice | 2020 | VLDB | 6.4933103e-05 |
| 6,045 | PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration | 2021 | SIGMOD | 5.9054678e-05 |
| 7,346 | Spade: A Real-Time Fraud Detection Framework on Evolving Graphs | 2023 | VLDB | 5.5461419e-05 |
| 9,368 | RUSH: Real-time Burst Subgraph Detection in Dynamic Graphs | 2024 | VLDB | 5.1868213e-05 |
| 10,132 | uBlade: Efficient Batch Processing for Uncertain Graph Queries | 2024 | SIGMOD | 5.0751052e-05 |
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