@thaliaent

This paper enhances graph embedding techniques for Sybil attack detection in decentralized networks. By incorporating temporal behavior patterns and multi-hop neighborhood features into node representations, we improve detection accuracy. Experiments on real-world blockchain datasets show 15% higher precision compared to traditional methods, effectively identifying malicious accounts while minimizing false positives.

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