## 4 Proposed Approach and Experiments
### Results and Evaluation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameter** & **Value** \\ \hline CNN & Convolution Layers & 3 \\  & Convolution filters & 64 \\  & Kernel Size & 3 \\  & Pool Size & 2 \\  & Activation & ELU \\ \hline GRU & GRU Layers & 3 \\  & Hidden Units & 512, 256, 128 \\  & Dropout & 0.3 \\  & Optimizer & Adam \\  & Learning Rate & 0.0001 \\  & Loss & Binary Crosentropy \\ \hline RF & n estimators & [50, 100, 200]—Best value: 100 \\  & max depth & [3, 5, 7]—Best value: 3 \\  & min samples split & [2, 5]—Best value: 2 \\  & min samples leaf & [1, 2]—Best value: 2 \\ \hline XGB & n estimators & [50, 100, 150]—Best value: 100 \\  & max depth & [3, 5, 7]—Best value: 7 \\  & learning rate & [0.01, 0.1, 0.2]—Best value: 0.1 \\ \hline Node2 Vec & Walk Length & 30 \\  & Walks per Node & 200 \\  & Embedding Dimension & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter configurations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **F-Measure** & **Precision** & **Recall** & **MAE** &
\begin{tabular}{c} **Cohen’s** \\ **Kappa** \\ \end{tabular} \\ \hline Node2 Vec + CNN-GRU-3 & 0.9675 & 0.9650 & 0.9660 & 0.9675 & 0.0325 & 0.94 \\ Node2 Vec + Ensemble L. & 0.9603 & 0.9590 & 0.9585 & 0.9603 & 0.0397 & 0.92 \\ GraphSAGE + CNN-GRU-3 & 0.9442 & 0.9430 & 0.9425 & 0.9442 & 0.0558 & 0.89 \\ GraphSAGE + Ensemble L. & 0.9563 & 0.9550 & 0.9545 & 0.9563 & 0.0437 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of classification performance of Node2 Vec and GraphSAGE embeddings with CNN-GRU-3 and ensemble learning. model performed better with 0.9675 accuracy, indicating that the random walk approach of Node2 Vec is more effective when combined with the powerful sequential and local information extraction capabilities of CNN-GRU-3. Table 6 shows the performance results obtained using the GAT classifier and Node2 Vec/GraphSAGE embeddings. The GAT model was evaluated both as a standalone classifier and combined with Node2 Vec and GraphSAGE embeddings. According to the results, the Node2 Vec + GAT model gave the best result with 0.9711 accuracy. When the results were examined, it was seen that the attention mechanism of GAT could process the contextual information more effectively when combined with Node2 Vec embeddings. After improving the node embeddings of Node2 Vec and GraphSAGE with GAT, they were classified with CNN-GRU-3 and ensemble learning classifiers. The results are shown in Table 7. GAT was used as a contextual enhancer, and the resulting embeddings were given to CNN-GRU-3 and ensemble learning classifiers. Node2 Vec + GAT + CNN-GRU-3 (proposed model) gave the best result among all groups with 0.9961 accuracy. In Table 8, GAT embeddings are classified with CNN-GRU-3 and ensemble learning. The GAT + CNN-GRU-3 model showed better performance than ensemble learning with 0.9713 accuracy. This situation revealed that CNN-GRU-3 is more effective compared to ensemble learning. The Node2 Vec + CNN-GRU-3 model proved to be an effective initial embedding technique with an accuracy value of 0.9675 for Node2 Vec, one of the components of the proposed model. However, when not combined with GAT and CNN-GRU-3, this model could not adequately handle contextual and sequential relationships. The Node2 Vec + GAT model showed that GAT's attention mechanism enriched the embeddings contextually with an accuracy of 0.9711. However, it performed less than the proposed model because it did not understand the sequential information with CNN-GRU-3. The GraphSAGE + GAT \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **F-Measure** & **Precision** & **Recall** & **MAE** &
\begin{tabular}{c} **Cohen’s** \\ **Kappa** \\ \end{tabular} \\ \hline GAT & 0.9388 & 0.9360 & 0.9375 & 0.9603 & 0.0612 & 0.87 \\ Node2 Vec + GAT & 0.9711 & 0.9700 & 0.9715 & 0.9711 & 0.0289 & 0.94 \\ GraphSAGE + GAT & 0.9703 & 0.9685 & 0.9690 & 0.9703 & 0.0297 & 0.93 \\ \hline \hline \end{tabular}
\end{table}