Under strict inductive protocols without temporal leakage, random forests on raw features achieve higher F1 scores than GNNs on Bitcoin fraud detection, and real graph structure can underperform random wiring.
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When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift
Under strict inductive protocols without temporal leakage, random forests on raw features achieve higher F1 scores than GNNs on Bitcoin fraud detection, and real graph structure can underperform random wiring.