# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 5 Experiment and Result Analysis
### Model Comparison Analysis  
This paper includes two experimental sections. The first section involves traditional supervised machine learning experiments, semi-supervised classification experiments based on the GCN model framework, semi-supervised classification experiments using recently proposed graph neural network models, and semi-supervised classification experiments using the enhanced model proposed in this paper, all performed on the Cora dataset. The classification results are compared and analyzed, as shown in Table 6. To provide a visual comparison of the accuracy trends for each model, bar graphs were created for the accuracy of each model, as shown in Figure 4. The second section highlights the application areas of the model proposed in this study for the Stock dataset and compares the ENode-GAT model proposed in this paper to existing models; the results are presented in Table 7. As depicted in Figure 5, a visualization of the model's accuracy was also created. In this comparison experiment, all of the previous experiment's results were selected for comparison and validation, and the following are the results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Split Ratio** & **1:2:5** & **2:3:3** & **1:4:3** & **6:1:1** \\ \hline \multirow{4}{*}{ENode-GAT-Cora} & Training Accuracy & 0.8500 & 0.8350 & 0.8500 & 0.8258 \\  & Validation Accuracy & 0.8233 & 0.8600 & 0.8375 & 0.8800 \\  & Test Accuracy & 0.8510 & 0.8460 & 0.8480 & 0.8800 \\  & Rounds & 270 & 298 & 299 & 295 \\ \hline \multirow{4}{*}{ENode-GAT-Stock} & Split ratio & 1:1:8 & 6:1:3 & 2:6:2 & 5:2:3 \\ \cline{2-6}  & Training Accuracy & 0.9000 & 0.9045 & 0.8625 & 0.9000 \\ \cline{1-1}  & Validation Accuracy & 0.8750 & 0.8750 & 0.8250 & 0.9000 \\ \cline{1-1}  & Test Accuracy & 0.8533 & 0.8767 & 0.8682 & 0.8912 \\ \cline{1-1}  & Rounds & 287 & 296 & 290 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy under different scaling conditions.  
Based on the classification results in Table 6 and the visualization in Figure 4, it is possible to conclude that GCN outperforms conventional machine learning models when solving certain semi-supervised node classification problems. The unique benefits of the graph neural network model for graph structure issues are highlighted. According to the results in the table, it can be seen that the classification accuracy of the model proposed in this paper is 85.1%, which is excellent and indicates that the model proposed in this paper has unique advantages.  
The six classification results for the Stock dataset are displayed in Table 7 and Figure 5, and it is evident that the model proposed in this paper has inherent advantages in managing this classification task. It exceeds the 2% classification precision of the previously proposed Stock model. This suggests that this model will be able to conduct similar classification tasks in the future and increase classification efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification Method** & **Accuracy** \\ \hline SVM & 77.7\% \\ Logistic Regression & 78.2\% \\ Random Forest & 77.9\% \\ GCN & 81.9\% \\ PA-GCN & 83.3\% \\ ENode-GAT & 85.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the classification results for the Stock dataset model.  
Figure 4: Bar chart of the classification results of the model in the Cora dataset.