SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.
Siege: Self-supervised incremental deep graph learning for ethereum phishing scam detection
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A recurrent E2F architecture with selective fusion and lightweight attention reports competitive reconstruction quality at lower model complexity on standard benchmarks.
citing papers explorer
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Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.
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Computation-Aware Event-to-Frame Reconstruction via Selective Attention
A recurrent E2F architecture with selective fusion and lightweight attention reports competitive reconstruction quality at lower model complexity on standard benchmarks.