## 4 Discussion
### Comprehensive Evaluation of Node Importance Based on PCA-RSR  
This study evaluates node importance across different network layers and dimensions (e.g., CS, SNC, DNC, etc.) using multiple proposed metrics. However, the results reveal discrepancies in rankings across the various metrics, with nodes exhibiting varying levels of importance depending on the metric applied. Hence, a comprehensive evaluation method is required to integrate these results and provide an overall assessment of node importance. The evaluation results show that TSs in the throat area are significantly more important than those in the arrival-departure tracks, connecting lines, or safety lines. Moreover, the left (downward throat) is generally more important than the right (upward throat), primarily because the YX network exclusively covers the downward throat area, giving an advantage to TSs in this region during node calculations and comprehensive ranking. To address potential data limitations and biases, this study proposes a comprehensive evaluation method for node importance based on PCA-RSR. PCA (Principal Component Analysis) [63; 64; 65] is employed as a commonly used dimensionality reduction technique to transform multiple metrics into a few integrated indicators. RSR (rank-sum ratio) [66] ranks benefit-type indicators in ascending order and cost-type indicators in descending order, calculates rank-sum ratios, and performs regression and graded ranking. The proposed evaluation method comprises two main parts:  
1. Single-layer network node importance evaluation based on PCA:  
PCA is applied to the five node importance metrics to extract three principal components. These components are aggregated to obtain the node importance evaluation results for a single network layer. 2. Multi-layer network comprehensive evaluation based on RSR:  
The RSR method is used to integrate and rank the PCA results from the three network layers, yielding the final comprehensive node importance rankings for the station topology network. The overall approach is illustrated in Figure 13.  
Figure 13: Comprehensive node importance evaluation process based on PCA-RSR. Using the proposed PCA-RSR-based comprehensive evaluation method for network node importance, the process was completed as follows:  
First, a single-layer network node importance evaluation based on PCA was performed. In this step, the five custom node importance metrics were transformed into three principal components, ensuring an information coverage rate exceeding 95%. Second, to improve algorithm efficiency, the three principal components were aggregated to obtain the node importance values for each network layer. Finally, based on the PCA-derived single-layer network node importance results, the RSR algorithm was applied to comprehensively evaluate the node importance across network layers. The results are shown in Figure 14.  
Figure 14 presents the results of the PCA-RSR-based comprehensive node importance evaluation for the LC, YX, and DC networks. The analysis consists of two main parts:  
* The lower section shows the PCA results for node importance within each network layer;
* The upper section displays the RSR results, providing a comprehensive ranking of node importance across all three network layers. In Figure 14, the horizontal axis represents the network nodes, while the vertical axis is divided into two sections. The lower section displays the PCA-derived node importance values for the LC (red circles), YX (orange squares), and DC (blue diamonds) network. The upper section illustrates the RSR-based comprehensive ranking results, categorized into three distinct levels: Level 1 (high-ranking nodes), Level 2 (moderate-ranking nodes), and Level 3 (low-ranking nodes). This dual-axis representation facilitates a comparative analysis of node importance across different networks and ranking methodologies. The lower section presents the PCA-derived analysis of node importance across the LC, YX, and DC networks.