activity
20202025
most citedxFraud: Explainable Fraud Transaction Detection

46 citations · 78 across the 12 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2024

Retrofitting Temporal Graph Neural Networks with Transformer

Qiang Huang, Xiao Yan, Xin Wang +5

Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TG…

cs.LG2022★ 1 cited

BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection

Mingxuan Lu, Zhichao Han, Susie Xi Rao +6

Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed…

cs.LG2022★ 5 cited

Modelling graph dynamics in fraud detection with "Attention"

Susie Xi Rao, Clémence Lanfranchi, Shuai Zhang +7

At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite th…

cs.LG2020★ 2 cited

Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks

Susie Xi Rao, Shuai Zhang, Zhichao Han +6

Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame. To monitor th…

cs.LG2020★ 46 cited

xFraud: Explainable Fraud Transaction Detection

Susie Xi Rao, Shuai Zhang, Zhichao Han +6

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud,…