# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 6 Conclusions  
This paper introduced MVCG-SPS, a novel Multi-View Contrastive Graph Neural Network designed to detect Ponzi schemes in smart contract transactions. By incorporating meta-path-based view construction, reinforcement-learning-driven aggregation, and multi-scale contrastive learning, MVCG-SPS effectively addresses the challenges posed by the heterogeneous and dynamic nature of blockchain interactions.The empirically chosen two-layer GCN structure addresses the performance degradation caused by over-smoothing in deeper architectures. Experimental evaluations on real-world Ethereum datasets demonstrated MVCG-SPS's superior performance over state-of-the-art baselines across F1 Score, AUPRC, and Rec@K metrics. The framework excelled at handling imbalanced datasets, highlighting its robustness in detecting subtle yet critical anomalies within  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **SHAP Value** & **Domain Significance** \\ \hline Upstream invitation density & +0.23 & Reflects pyramid recruitment patterns \\ Investment/payment ratio & +0.19 & Indicates unsustainable returns \\ \hline \hline \end{tabular}
\end{table}
Table 6: SHAP value analysis for representative case (Ponzi scheme identification).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Nodes** & **Training Time (s/epoch)** & **GPU Memory (GB)** & **F1 Score** \\ \hline
10K & 12.3 & 0.2 & 0.901 \\
100K & 124.7 & 0.3 & 0.895 \\
500K & 618.4 & 0.4 & 0.887 \\
1M & 1235.1 & 0.5 & 0.881 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Scalability analysis on synthetic graphs.  
blockchain transactions. Ablation studies further confirmed the essential contributions of the meta-path view construction and multi-scale contrastive learning mechanisms. While MVCG-SPS is designed for platform-agnostic fraud detection, its current validation remains limited to Ethereum and Ponzi scheme detection. Real-world deployment challenges and the need for enhanced explainability (e.g., causal analysis) require further investigation. Future work will focus on extending MVCG-SPS to diverse blockchain ecosystems (e.g., Solana, Binance Smart Chain) by adapting modular components to platform-specific interactions and emerging fraud patterns (e.g., phishing, money laundering), while developing adaptive meta-path frameworks with continual learning to automate dynamic adjustments. Additionally, we plan to optimize real-time deployment through model pruning and incremental learning, validate operational feasibility via DeFi platform partnerships, and prioritize cross-chain fraud detection alongside IoT security applications for evolving decentralized systems.  
Conceptualization, data curation, methodology, software, writing, XJ.; validation, W.-T.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Tiande Company. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.