@silasent

Money laundering on blockchains exploits complex transaction graphs. This method uses graph neural networks (GNNs) to detect suspicious patterns (e.g., cycling, mixing). Feature extraction aggregates node degrees, edge weights, and temporal patterns, while attention mechanisms highlight critical paths. Experiments on Ethereum datasets achieve 88% accuracy in identifying laundering rings, aiding regulatory compliance in decentralized finance.

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