An MLP with SMOTE and Focal Loss is claimed to detect rug pull tokens with 0.927 accuracy, but the ground truth labels are partly randomly generated.
Atomgraph: Tackling atomicity violation in smart contracts using multimodal gcns.Proceedings of the IEEE/ACM 48th International Conference on Software Engineering, 2025
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From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification
An MLP with SMOTE and Focal Loss is claimed to detect rug pull tokens with 0.927 accuracy, but the ground truth labels are partly randomly generated.