## 4 Results and Analysis
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Method** & **Cora** & **Citeseer** & **PubMed** & **Wiki-CS** & **AMZ Computers** \\ \hline MLP & \(64.6\pm 1.7\) & \(62.0\pm 1.8\) & \(85.9\pm 0.3\) & \(73.17\pm 0.19\) & \(44.9\pm 5.8\) \\ LR & \(77.3\pm 1.8\) & \(71.2\pm 1.8\) & \(86.0\pm 0.6\) & - & - \\ LPA(2005) [46] & \(85.3\pm 0.9\) & \(70.0\pm 1.7\) & \(82.6\pm 0.6\) & - & - \\ GCN(2017) [2] & \(88.2\pm 0.8\) & \(77.3\pm 1.5\) & \(87.2\pm 0.4\) & \(79.07\pm 0.10\) & \(82.6\pm 2.4\) \\ GAT(2018) [29] & \(87.7\pm 0.3\) & \(76.2\pm 0.9\) & \(86.9\pm 0.5\) & \(79.63\pm 0.10\) & \(78.0\pm 1.9\) \\ JK-Net(2018) [47] & \(89.1\pm 1.2\) & \(78.3\pm 0.9\) & \(85.8\pm 1.1\) & - & - \\ GCN-LPA(2020) [28] & \(88.5\pm 1.5\) & \(78.7\pm 0.6\) & \(87.8\pm 0.6\) & - & - \\ Bet-GAT(Proposed) & \(89.15\pm 1.5\) & \(79.0\pm 0.3\) & \(87.5\pm 0.5\) & \(85.73\pm 0.3\) & \(91.05\pm 1.3\) \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental results. Figure 8: Graphical representation for Citeseer dataset. Figure 7: Graphical representation for Cora dataset. In Figure 10, we show the accuracy and loss curves for the Wiki-CS dataset. The corresponding TSNE GAT embeddings (graphical representation) show clusters for the ten different classes. For the training and validation phases, the accuracy and loss curves are plotted (epochs are plotted on the \(x\)-axis and accuracy and loss on the \(y\)-axis). We obtain an accuracy of 85.073% with a loss of 0.4639 for Wiki-CS. Finally, in Figure 11 we have presented the graphs of accuracy and training loss for the AMZ computers. The corresponding TSNE GAT embeddings show clusters for its three classes. For training and validation, the accuracy and loss graphs are plotted (\(x\)-axis shows epochs and \(y\)-axis shows accuracy and loss). We obtain an accuracy of 91.05% for Amazon with a loss of 0.2641. For verification of our proposed model, we calculate the generalization error that refers to the test error of the model, which indicates how well a model generalizes to the unseen data. We experimentally analyzed the training and test errors for the mentioned datasets and observed that even though test error tends to be greater than training error loss, the difference is not very significant. In the case of Cora, the training error and test error loss values are 0.3712 and 0.25, respectively, with test accuracy  
Figure 10: Graphical representation for Wiki-CS dataset. Figure 9: Graphical representation for PubMed dataset. being 89.15%. For Citeseer, the training error and test error loss values are 0.4143 and 1.2, respectively, with test accuracy being 79%. In the case of Citeseer, the training to test error loss is high which accounts for low accuracy of the model over this dataset as compared to CORA. In addition, it can be observed that the loss value difference is not huge and due to which model performs well to classify the unseen node labels. For CORA, the base GAT model has training and test loss values ranging above 0.5 or more attaining an accuracy of around 85%. So, here the loss increases, and also the difference between the training to test error loss is marginally high. With respect to the model complexity, our proposed model just adds up a step of identifying the nodes based on the betweenness centrality which is a preprocessing step and a one time evaluation task for the nodes of the graph. So, it does not add up to the overall complexity of the model for predicting the node labels. Thus, Bet-GAT has the same model complexity as the base GAT model and a minimized generalization error which makes it a suitable choice to prefer. The F1 score is an indicative measure of the accuracy of the model. The F1 scores (calculated using different methods) for the five benchmark datasets were compared with the other state-of-the-art methods. The score gained through the training are as follows: Cora (87%), Citeseer (84%), PubMed (84%), Wiki-CS (86%), and AMZ computers (95%) (see Table 3).