## 4 Results and Analysis
### Experimental Results  
The results of our proposed model are summarized in Table 2. In terms of performance (accuracy), our proposed Bet-GAT model (betweenness centrality with graph attention network) is in line with the state of the art for the five benchmark networks: Cora, Citeseer PubMed, Wiki-CS, and AMZ Computers. For node classification, methods, such as multilayer perceptron (MLP) and logistic regression (LR), consider only node features. On the other hand, a method such as the label propagation algorithm (LPA) focuses only on graph structure [46]. Overall, these three methods do not fully utilize the underlying network (dataset) (either in terms of structural properties or node features). This is the main reason for the lower accuracy of these methods (see Table 2). In the case of GCN [2] and GAT methods [29], we find that they provide better results in different citation networks compared to the previously discussed methods. We have already justified why our proposed method performs better than the traditional GCN/GAT model. In other methods, such as jumping knowledge networks (JK-Net) [47] and GCN- label propagation algorithm (GCN-LPA) [28], we find that JK-Net works on subgraphs with distinct local structures generated based on random walks. JK-Net is a strong foundation for Cora, but it does not provide the same for other datasets. The GCN-LPA model allows simultaneous learning of transformation matrices and edge weights. Therefore, it provides better performance than the traditional GCN. With our proposed GAT implementation, we achieve a classification accuracy of 89.15% for Cora, 81% for Citeseer, and 87.5% for PubMed. These results exceed the classification accuracy of the other state-of-the-art-based GCN implementations [28] (see Table 2). Our proposed model focuses on the structural properties of the underlying graph for feature generation and prediction. Bet-GAT architectural hyperparameters were fine-tuned for the Cora, Citeseer, and PubMed datasets. We consider the ratio of 6:3:1 for the partitioning of the training set, the validation set, and the test set. We use a two-layer GAT model. For the first layer, we use 32 attention heads, each computing 128 hidden node features. The label classification result is provided by the second layer. Then, we use ELU (exponential linear unit) [40] followed by Softmax activation. We also apply dropout = 0.5 for both layers. During training, we apply L2 regularization with a learning rate of 0.0005. The weight of each edge is treated as a free variable during training. We train our model for 1000 epochs using Adam [48]. The same hyperparameters are applied to all three datasets  
In Figures 7-9, we have shown the graphical representations of the accuracy and loss curves for the Cora, Citeseer, and PubMed citation networks. The graphical representations of the datasets in the form of TSNE embeddings show clusters of different classes (seven, six, and three clusters for Cora, Citeseer, and PubMed, respectively). Accuracy and loss curves are plotted for the training and validation phases. For the graphs, the epochs are shown on the \(x\)-axis, and the accuracy and loss are shown on the \(y\)-axis. We obtain 89.15% accuracy with loss 0.3712 for CORA, 79.00% accuracy with loss 0.4143 for Citeseer, and 81.00% accuracy with loss 0.3400 for PubMed.