Researchers seek help with underperforming fraud detection GNN model

A user on Reddit is seeking assistance with a Graph Neural Network (GNN) model designed for fraud detection. Despite implementing feature engineering and constructing a heterogeneous graph using the IEEE CIS Fraud Detection Dataset, the model's performance, measured by AUC, PR-AUC, recall@5%, and precision@5%, is not meeting expectations compared to state-of-the-art models. The user has tried GCN, GraphSAGE, and GAT, all yielding similar results, and is asking for guidance on potential issues. AI

RANK_REASON User is seeking help with a research paper on a GNN model for fraud detection, indicating a research context. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/MachineLearning →

• GAT
• Graph Neural Network
• GraphSAGE
• IEEE CIS Fraud Detection Dataset

• paper
• other

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