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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)

Paper ID
h2322c0b5d972437f
Venue
VLDB
Year
2023
Pagerank
5.1176637e-05
Overall Rank
9,876 | 33.60%
DOI
10.14778/3611540.3611551

Incoming Non-self Citations Over Time

Authors

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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