# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Results and Analysis
#### 5.4.3 Sensitivity Analysis  
To investigate how hyperparameter choices affected the performance of MVCG-SPS, we focused on three critical aspects: meta-path selection, the number of meta-path views \(V\) in the meta-path-based view decomposition, and the reward discount factor \(\gamma\) in the reinforcement-learning-driven aggregation.  
All hyperparameters were tuned using an exhaustive grid search (and Bayesian optimization for selected parameters) on a dedicated validation set. For each parameter, we defined a reasonable search space, informed by preliminary experiments and prior work (see Table 4). Models were trained on the training data, with a reserved validation subset used solely for evaluation, and the optimal configuration was chosen based on the highest validation accuracy.  
Our analysis of meta-path selection revealed that alternative configurations significantly impacted the detection performance. Replacing \(P_{2}\) in the interaction enhancement module with a meta-path emphasizing temporal transactions,  
\[\text{CAt}\xrightarrow{\text{pay}}\text{EOA}\xrightarrow{\text{invite}}\text {EOA},\]  
resulted in a 3.1% reduction in F1 Score (from 0.902 to 0.874) and a similar drop in AUPRC (from 0.891 to 0.863). In contrast, adding an extra sampling meta-path,  
\[\text{EOA}\xrightarrow{\text{trans}}\text{EOA}\xrightarrow{\text{call}}\text {CA},\]  
improved the F1 Score by 2.4% (to 0.924) and increased AUPRC to 0.912, although this incurred an 18% increase in runtime. These findings, summarized in Table 5, underscore that, while alternative meta-paths can offer marginal improvements in some metrics, they also introduce trade-offs in computational cost. Our current configuration thus represents a balanced trade-off between detection performance and efficiency, while remaining adaptable to other fraud detection scenarios.  
The number of meta-path views \(V\) was varied over the set \(\{2,3,5,7\}\). Increasing \(V\) from 2 to 5 yielded significant performance improvements, because additional views captured richer semantic information. However, when \(V\) was increased to 7, the performance plateaued or slightly declined, likely due to redundancy and the introduction of noise. Similarly, we evaluated the reward discount factor \(\gamma\) over \(\{0.5,0.7,0.9,0.99\}\). Our experiments indicated that increasing \(\gamma\) generally enhances performance, with the optimal value found to be 0.9; values above 0.9 lead to marginal decreases, as the model becomes less sensitive to short-term reward signals. Figure 3 illustrates the sensitivity of MVCG-SPS with respect to both \(V\) and \(\gamma\).  
In summary, our sensitivity analysis highlighted the critical role of hyperparameter tuning. The experiments demonstrated that preserving investment-payment semantics in meta-path design is essential. A moderate number of meta-path views (approximately 5) captures diverse semantic nuances without introducing redundancy, while an optimal reward discount factor of 0.9 effectively balances long-term and short-term rewards. These insights confirm the robustness of MVCG-SPS to parameter variations and provide a solid reference for future research in multi-view GNN-based blockchain fraud detection.