MINT: Detecting Fraudulent Behaviors from Time-series Relational Data
Summary: MINT models each user’s time-series relational data as a time-aware behavior graph, learning short-, medium-, and long-term intentions via multiview graph convolutions. Gated row-level interactions improve fraud detection, interpretability, and scalable training on billions of records. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fei Xiao (National University of Singapore; Shopee Singapore)
- 2. Yuncheng Wu (National University of Singapore)
- 3. Meihui Zhang (Beijing Institute of Technology)
- 4. Gang Chen (Zhejiang University)
- 5. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@article{xiao_vldb23,
title = {{MINT: Detecting Fraudulent Behaviors from Time-series Relational Data}},
author = {Xiao, Fei and Wu, Yuncheng and Zhang, Meihui and Chen, Gang and Ooi, Beng Chin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3610--3623},
doi = {10.14778/3611540.3611551},
url = {https://doi.org/10.14778/3611540.3611551},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,805 | TVA: A Version-aware Temporal Graph Storage System for Real-time Analytics | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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