Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains

Researchers developed a scalable graph screening method capable of processing 835 million addresses to identify high-risk entities, with a label-free transfer approach that generalizes to new blockchain networks without retraining. The technique combines population-level calibration with machine learning to reduce false positives while maintaining detection accuracy across different data distributions.

Researchers developed a scalable graph screening method capable of processing 835 million addresses to identify high-risk entities, with a label-free transfer approach that generalizes to new blockchain networks without retraining. The technique combines population-level calibration with machine learning to reduce false positives while maintaining detection accuracy across different data distributions. This addresses a critical challenge in financial crime detection and regulatory compliance at unprecedented scale.

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