# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Results and Analysis
#### 5.4.2 Ablation Study  
To understand the contribution of each module in MVCG-SPS, we performed an ablation study by systematically removing key components of the model. The configurations analyzed included the following:  
1. MVCG-SPS (Full Model): The complete model with all modules. 2. MVCG-SPS w/o RL: Removing reinforcement-learning-driven multi-view aggregation. 3. MVCG-SPS w/o MSCL: Excluding the multi-scale contrastive learning module. 4. MVCG-SPS w/o MPBV: Removing the meta-path-based view decomposition module.  
Table 3 presents the results of the ablation study. The full MVCG-SPS model achieved the highest scores across all metrics, underscoring the effectiveness of each integrated module.  
1. MVCG-SPS (Full Model): The complete MVCG-SPS model incorporated meta-path-based view decomposition, reinforcement-learning-driven multi-view aggregation, and multi-scale contrastive learning modules. This configuration achieved the best performance, with an F1 Score of 0.902, an AUPRC of 0.891, and a Rec@K of 0.871. The synergy of these modules allows the model to capture semantic diversity, dynamically adjust view contributions, and enhance feature alignment, culminating in superior anomaly detection capabilities.  
2. MVCG-SPS (w/o RL): Removing the reinforcement-learning-driven multi-view aggregation module resulted in a decrease in F1 Score to 0.879, AUPRC to 0.861, and Rec@K to 0.843. This decline of approximately 3.4% in F1 Score and 3.2% in Rec@K highlights the critical role of adaptive weight adjustment in effectively merging multi-view features. Without the RL aggregation module, the model struggled to prioritize important views, leading to suboptimal feature fusion and reduced detection performance.  
3. MVCG-SPS (w/o MSCL): Excluding the multi-scale contrastive learning module led to an F1 Score of 0.868, an AUPRC of 0.853, and a Rec@K of 0.835. The absence of this module diminished the model's ability to align and integrate global and local features, thereby weakening its robustness and capacity to detect complex interaction patterns within the data.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Configuration** & **F1 Score** & **AUPRC** & **RecK** \\ \hline MVCG-SPS (Full Model) & **0.902** & **0.891** & **0.871** \\ MVCG-SPS w/o RL & 0.879(\(-\)2.6\%) & 0.861(\(-\)3.4\%) & 0.843(\(-\)3.2\%) \\ MVCG–SPS w/o MSCL & 0.868(\(-\)3.8\%) & 0.853(\(-\)4.3\%) & 0.835(\(-\)4.1\%) \\ MVCG–SPS w/o MPBV & 0.854(\(-\)5.3\%) & 0.840(\(-\)5.7\%) & 0.822(\(-\)5.6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results4. MVCG-SPS (w/o MPBV): The removal of the meta-path-based view decomposition module resulted in the most significant performance drop, with the F1 Score decreasing to 0.854, AUPRC to 0.840, and Rec@K to 0.822. This substantial decline underscores the importance of meta-path-based view decomposition in capturing the multifaceted semantic relationships within blockchain data. Without this module, the model lost its ability to analyze transaction behaviors from multiple perspectives, severely impairing its anomaly detection effectiveness.  
The results unequivocally demonstrate that each module within MVCG-SPS contributes significantly to the overall performance. The meta-path-based view decomposition enhances semantic modeling by capturing hierarchical invitation chains and investment cycles, while the RL-driven aggregation optimizes view weight contributions, and the multi-scale contrastive learning ensures effective feature alignment. Notably, removing meta-path decomposition caused the largest performance degradation (5.3% F1 drop), emphasizing its role as the cornerstone of the framework's ability to model Ethereum-specific fraud behaviors.