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Found 9 papers, 4 papers with code
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
Behavioral graph fraud detection in E-commerce
Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios.
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
Modelling graph dynamics in fraud detection with "Attention"
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions.
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces.
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame.
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
