# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Results and Analysis  
#### 5.4.1 Performance Evaluation  
To evaluate the performance of MVCG-SPS, we compared its results with state-of-the-art baseline models. Table 2 presents a performance comparison of the MVCG-SPS model against several baseline models on the Heterogeneous Ghet dataset. The evaluation metrics included F1 score, AUPRC, and RecK, which comprehensively assessed the anomaly detection performance from the perspectives of overall performance, adaptability to class imbalance, and high-risk detection capability. 1. Overall Performance (F1 Score): The F1 score of MVCG-SPS reached 0.902, significantly higher than the highest score of 0.889 achieved by the MERIT model. The F1 score, as the harmonic mean of precision and recall, reflected the model's ability to balance false positives and false negatives. Compared to the baseline models, the high score of MVCG-SPS indicated stronger robustness and generalization in accurately identifying Ponzi scheme transactions. 2. Adaptability to Class Imbalance (AUPRC): MVCG-SPS also excelled in the AUPRC metric, achieving a score of 0.891, which was about 2.2% higher than the closest competitor, MERIT (0.872). Since AUPRC focuses on the precision of the positive class rather than overall accuracy, it is more representative for datasets with severe class imbalances. The advantage of MVCG-SPS is attributed to its multi-view feature aggregation and contrastive learning mechanism, which effectively capture key information from multiple views, such as transaction frequency and call relationships, thereby enhancing anomaly detection in complex data distributions. 3. High-Risk Priority Detection (Top-K Recall): In real-world scenarios, the early detection of high-risk anomalies is critical. MVCG-SPS also achieved the highest score for the RecK metric, with a value of 0.871, 1.6% higher than the nearest competitor, MERIT. Top-K recall evaluates a model's ability to successfully identify anomalies among the high-risk transactions ranked at the top. This result demonstrates that MVCG-SPS can effectively filter out potential high-risk Ponzi schemes in the early stages, providing a more reliable basis for subsequent interventions and decisions. While the traditional ML methods (XGBoost, RF) achieved moderate performance, their reliance on manual feature engineering limits their capability to capture multi-step fraud patterns. MVCG-SPS outperformed non-graph baselines like Random Forest \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Category** & **F1 Score** & **AUPRC** & **RecK** \\ \hline Machine Learning Methods: & & & \\ RF & 0.746 & 0.728 & 0.692 \\ XGBoost & 0.771 & 0.752 & 0.718 \\ \hline Homogeneous Graph Methods: & & & \\ GCN & 0.812 & 0.789 & 0.751 \\ SGC & 0.801 & 0.771 & 0.733 \\ GIN & 0.831 & 0.810 & 0.792 \\ GraphSAGE & 0.845 & 0.827 & 0.811 \\ GAT & 0.843 & 0.824 & 0.812 \\ \hline Heterogeneous Graph Methods: & & & \\ RGCN & 0.854 & 0.835 & 0.822 \\ RGAT & 0.878 & 0.861 & 0.846 \\ HGT & 0.865 & 0.847 & 0.830 \\ \hline Perturbation-Based Methods: & & & \\ MVGRL & 0.876 & 0.860 & 0.841 \\ MERIT & 0.889 & 0.872 & 0.855 \\ \hline Proposed Method: & & & \\
**MVCG-SPS (Ours)** & **0.902** & **0.891** & **0.871** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison across blockchain fraud detection methods(F1: +13.1%) and XGBoost (F1: +12.1%), demonstrating the necessity of graph-based modeling for relational fraud detection. A superior AUPRC (0.891 vs. 0.752) further confirmed that graph-based learning better handled class imbalance by exploiting topological anomaly signals. Compared to traditional graph neural networks (e.g., GCN and SGC), MVCG-SPS showed a clear advantage, indicating that simple single-view methods struggle to capture the complex interaction patterns in blockchain data. Additionally, compared to models that support heterogeneous graphs (e.g., HGT and RGCN), MVCG-SPS showcased its unique advantage in multi-view feature decomposition and adaptive aggregation.