## 5 Experimental Setup
### Hyperparameter Tuning Experiment  
We use grid search to obtain the optimal hyperparameter configuration. Then, we fix the remaining hyperparameters and individually adjust each one, observing its impact on the model's performance. This helps us to assess the importance of each hyperparameter on model performance, optimize the hyperparameter selection process, and enhance the model's robustness and usability. For model performance optimization, we design a series of hyperparameter tuning experiments to explore the impact of learning rate, model depth, the number of attention heads, and loss function weights on performance. The experiments are conducted on three representative and diverse datasets: JK, IST, and KL. The experimental results are summarized as follows:  
As shown in Figure 8, when investigating the impact of the learning rate on the model, we find that a learning rate of \(1\times 10^{-4}\) significantly improves the AUC and AP metrics, allowing the model to quickly converge to a high-performance state, making it the optimal choice. A learning rate of \(1\times 10^{-5}\) shows limited performance improvement, while \(1\times 10^{-6}\) is too low, leading to poor training results and almost no improvement in model performance. As shown in Figure 9, when investigating the impact of the number of propagation layers on the model, both the AUC and AP metrics reach high and stable performance levels for the KL, IST, and JK datasets when the number of propagation layers is set to 3. Too few layers lead to insufficient feature extraction, while the 3-layer setup allows for the sufficient capture of data features. Increasing the layers beyond 3 may cause overfitting, increase computational complexity, and offer limited performance improvement. Therefore, the 3-layer propagation configuration is the optimal choice for balancing model performance and resource consumption. Figure 7: The trend of the Top@K metric of GEVEHGAN across six datasets with the training epochs. (**a**–**f**), respectively, depict how the Top@K performance of the GEVEHGAN model evolves as the number of training epochs increases on the IST, JK, KL, NYC, SP, and TKY datasets. As shown in Figure 10, when investigating the impact of the number of attention heads in the model's attention mechanism, we find that the KL dataset is insensitive to the configuration of the number of heads, with stable and good performance at 4 heads; the IST dataset exhibits significant dependence on the number of heads, reaching peak AUC and AP at 4 heads; the JK dataset's performance improves with an increasing number of heads, being optimal at 8 heads, but nearly reaching the peak at 4 heads. Overall, a 4-head configuration effectively balances the performance across the KL, IST, and JK datasets, making it the optimal strategy for enhancing the model's prediction performance across different datasets. Figure 8: Analysis of the impact of learning rate on model performance metrics. (**a–c**) show the changes in the AUC metric with epochs for the GEVEHGAN model on the KL, JK, and IST datasets at different learning rates (\(1\times 10^{-4}\), \(1\times 10^{-5}\), \(1\times 10^{-6}\)). (**d–f**) display the changes in the AP metric with training epochs for these three datasets under the same learning rate settings. Figure 9: Analysis of the impact of propagation layers on model performance metrics. (**a**) shows how the AUC metric of the GEVEHGAN model changes with the increase in propagation layers on the KL, JK, and IST datasets; (**b**) displays the trend of the model’s AP metric as the number of propagation layers changes on these three datasets. As shown in Figure 11, when exploring the impact of loss function weight values on the model, we find that adjusting the weight significantly affects AP, while having a smaller impact on AUC. Properly increasing the weight of the KL divergence loss can improve AP, but excessive adjustment harms convergence.