MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection

MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection

Blog Article

Detecting fraudulent activities such as Ponzi schemes within smart contract transactions is a critical challenge in decentralized finance.Existing methods often fail to capture the heterogeneous, multi-faceted nature of blockchain data, and many graph-based models overlook the contextual patterns that are vital for effective anomaly detection.In this paper, we propose MVCG-SPS, a Multi-View Contrastive Graph Neural Network Cartridges designed to address these limitations.Our approach incorporates three key innovations: (1) Meta-Path-Based View Construction, which constructs multiple views of the data using meta-paths to capture different semantic relationships; (2) Reinforcement-Learning-Driven Multi-View Aggregation, which adaptively combines features from multiple views by optimizing aggregation weights through reinforcement learning; and (3) Multi-Scale Contrastive Learning, which aligns embeddings both within and across views to enhance representation robustness and improve anomaly detection performance.By leveraging a multi-view strategy, MVCG-SPS trikes effectively integrates diverse perspectives to detect complex fraudulent behaviors in blockchain ecosystems.

Extensive experiments on real-world Ethereum datasets demonstrated that MVCG-SPS consistently outperformed state-of-the-art baselines across multiple metrics, including F1 Score, AUPRC, and Rec@K.Our work provides a new direction for multi-view graph-based anomaly detection and offers valuable insights for improving security in decentralized financial systems.