## 3 Route Planning Scheme Based on Scatter-GNN
### Problem Definition
The specific parameters are set as follows: Node2 vec algorithm random walk step size is 40, random walk step number is 80, parameter b is 0.25, parameter d is 2, and the window size is 10. The GAT model has 32 attention layers and 64 hidden layer nodes. All GNN models have an Epoch of 100 and a learning rate of \(1\times 10^{-5}\). All module parameters in the Scatter-GNN model are consistent with the GAT model. All algorithms are trained on 90% of the data and tested on 10% of the data. ### Experimental Analysis

Classification accuracy (ACC) and F1-Score are important indicators for evaluating the performance of classifiers, which have been widely used in the industry in recent years. Therefore, this paper uses these two evaluation indicators to evaluate the model. For processing edge graph structures, the Macro-F metric is more comprehensive in evaluating the model. Table 2 presents the experimental results of some commonly used baseline algorithms and Scatter-GNN models on this dataset. As can be seen from the table, the traditional graph embedding method has a large gap in prediction performance compared with the deep learning model. However, Scatter-GNN and other graph neural network models have no significant gap in accuracy and F1-Score. This is because the model has always been based on the assumption of the number of sites \(n\rightarrow\infty\) in the calculation of the adaptive function. Moreover, when the number of sites is small, the function value is small, the number of edge sites is generally small, and the number of potential neighbors is also small. This results in a small difference between the completed line network structure and the original network, which can be approximately treated as the same type of GNN model. Nevertheless, Scatter-GNN has an improvement in accuracy compared with the GAT model. In addition, this paper also uses the GNN model to calculate the existence probability of Pingliang-Tianshui-Guangyuan and Pingliang-Baoji-Hanzhong lines, as shown in Figure 5 below:

The Scatter-GNN classifier calculates the connection probabilities between two or more candidate lines and compares their results. Several routes with higher total probabilities are included in the route planning to improve the site selection strategy. As can be seen from the figure above, there is a certain gap in the probability calculation values of the four routes for various GNN models. However, their judgments on the final result are completely consistent. Among them, the Scatter-GAT model is used to calculate the existence probabilities of the four routes of Pingliang-Tianshui, Tianshui-Guangyuan, Pingliang-Baoji, Baoji-Hanzhong are about 62.06%, 66.84%, 75.15%, and 71.11%, respectively. This also shows that Baoji and Hanzhong are more likely to become node stations in this north-south railway trunk line. In the same way, this method is also applicable to the planning of other high-speed rail lines. With the gradual increase in known routes, the prediction of the model is more convincing. ## 5 Conclusions

In this paper, a graph neural network is used as the basic model, and a classification model for solving the edge network structure is proposed. China's high-speed railway line network is a typical edge graph structure network, so the model proposed in this paper can be applied to traditional line planning. Experiments show that the line of Pingliang-Baoji-Hanzhong section has a higher probability of existence, and it is more likely to become a node station on this north-south railway trunk line. Due to the extremely limited number of railway stations, the prediction accuracy of the graph deep learning model is not high. In addition, the calculation of the adaptive function of the model is based on the assumption that the number of sites \(n\rightarrow\infty\) makes Scatter-GNN and other graph neural network models have no significant difference in accuracy and F1-Score. Therefore, the Scatter-GNN model proposed in this paper is only used as an auxiliary strategy for traditional route