## 4 Experimental Results and Discussion
### Ablation Experiment  
In this section, we investigated the advantages of individually increasing the ENN undersampling and attention mechanisms, as well as simultaneously increasing the ENN undersampling and attention mechanisms, on model performance. The experiments were conducted with well-defined constraints, including an imbalance ratio set to 0.5, loss weight  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Dataset and Metric** & **Cora** & & **Citeseer** & & **PubMed** \\
**AUC-ROC** & **F1-Macro** & **AUC-ROC** & **F1-Macro** & **AUC-ROC** & **F1-Macro** \\ \hline GCN & 0.8543 & 0.5205 & 0.6334 & 0.4503 & 0.8154 & 0.5501 \\ GraphSAGE & 0.8687 & 0.5376 & 0.707 & 0.4743 & 0.8332 & 0.5721 \\ DR-GCN & 0.8776 & 0.5513 & 0.6102 & 0.3924 & 0.6714 & 0.5628 \\ GraphSmote & 0.8942 & 0.5960 & 0.7193 & 0.3987 & 0.8457 & 0.6881 \\ ESA-GCN & **0.8997** & **0.6392** & **0.7614** & **0.4475** & **0.8746** & **0.7229** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different methods for classifying imbalanced nodes.  
set to 0.1, and undersampling neighbor count K set to 3. Each experiment was repeated three times, and the results were averaged. Table 2 presents the average results.  
From Table 2, we observed the following points:  
After adding ENN undersampling during the sampling phase,  
1. Compared to the original model, the Cora, Citeseer, and PubMed datasets showed improvements in both AUC-ROC and F1 scores. This indicates that the ENN algorithm effectively reduces the classifier's error rate and improves its performance stability by removing low-quality and noisy samples.
2. The ENN undersampling algorithm identifies unreasonable samples by calculating the distances between each sample and its nearest neighbors. Removing these unreasonable samples can reduce the data volume, alleviate the burden of model training, and slightly improve the training speed. However, due to the added undersampling process, the running speed of the Cora dataset is slightly slower than the original model, while the running speeds of the Citeseer and PubMed datasets are faster than the original model.  
After adding attention mechanisms during the edge generation phase,  
1. Compared to the original model, the Cora, Citeseer, and PubMed datasets showed improvements in both AUC-ROC and F1 scores. This demonstrates that attention mechanisms can better consider the relationships and feature interactions between nodes, resulting in more accurate node aggregation on the graph.
2. The running time also decreased, indicating that with the inclusion of attention mechanisms, the model can concentrate on processing key information with higher weights in the input data while ignoring other irrelevant information, thus significantly reducing model parameters and computational complexity.  
After simultaneously adding both ENN undersampling and attention mechanisms,  
1. The original model generalized minority class nodes too much without considering reducing majority class nodes. The ENN algorithm reduces noisy data by removing unimportant or erroneous samples. Therefore, the samples obtained through SMOTE-ENN sampling are cleaner and can reduce interference during model training.
2. The original model used GraphSage and node similarity calculation during the edge generation phase, which was slower and did not fully consider the interrelationships between nodes.  
Overall, our proposed ESA-GCN model showed improvements in AUC-ROC, F1 scores, and running speed compared to the original GraphSnote model.