## 3 Methodology
### Network Topology Analysis Methodology
[PERSON] (2002) introduced the concept of degree assortativity to quantitatively analyze the tendency of nodes with similar degree to form connections [70]. [PERSON] et al. (2007) demonstrated through the analysis of collaboration networks that the assortativity metric is effective in explaining the structure and evolution of network connectivity and that the presence of assortativity in real-world networks reflects meaningful structural characteristics [71]. This measure enables the evaluation of whether a network exhibits homophily (high assortativity, where nodes of similar degree tend to connect) or heterophily (low assortativity, where nodes of different degrees are more likely to be connected). \[Degree\ Assortativity\ =\ \frac{\sum_{ij}\bigl{(}A_{ij}k_{i}k_{j}\bigr{)}-\frac{1}{M} \sum_{i}k_{i}^{2}}{\sum_{i}k_{i}^{2}-\frac{1}{M}\sum_{i}k_{i}^{2}} \tag{7}\]  
\(k_{i}\): Degree of node \(i\)  
Aij: 1 if nodes \(i\) and \(j\) are connected; otherwise, 0;  
M: Number of overall edges. [PERSON] (2003) proposed the concept of transitivity, which measures the proportion of triangular structures at the global network level [72]. While transitivity is conceptually similar to the clustering coefficient, it is computed for the entire network rather than individual nodes. A recent study analyzing public transportation networks from the perspective of complex systems demonstrated that transitivity is effective in quantifying the cohesiveness of local structures and the hierarchical characteristics of networks [73]. This metric was shown to serve as a critical indicator for understanding structural differences between hub and non-hub nodes [73]. A high transitivity value indicates a greater prevalence of triangular formations, suggesting a strong overall clustering tendency within the network. \[Transitivity\ =\ \frac{3\times Number\ of\ triangles}{Number\ of\ connected\ triplets} \tag{8}\]  
[PERSON] (2006) introduced modularity as a quantitative measure of community structure in networks [74]. Modularity evaluates the extent to which a network is partitioned into distinct communities, where high modularity values indicate well-defined groups of nodes. A recent study analyzing the global maritime transportation network employed the modularity metric to identify region-based modular structures and the strategic importance of gateway hubs connecting them. The study demonstrated that this modular configuration has a strong correlation with overall network integration and functional performance (e.g., trade volume) [75]. Given the demonstrated relevance of modularity in understanding network integration and performance, this study adopts it as a metric to assess the structural characteristics of functional subgroups and to identify strategically significant vertiport locations. This metric captures the balance between intra-community links (connections within the same community) and inter-community links (connections between different communities), with higher values implying stronger community structures characterized by dense internal connectivity and sparse external connections. \[Modularity\ =\ \frac{1}{2M}\sum_{ij}\bigl{[}A_{ij}-\frac{k_{i}k_{j}}{2M} \bigr{]}\times\delta(c_{i},c_{j}) \tag{9}\]  
Aij: Adjacency matrix indicating whether nodes \(i\) and \(j\) are connected;  
\(k_{i,j}\): Degree of node \(i\) and \(j\);  
M: Number of overall edges;  
\(\delta(c_{i},c_{j})\): 1 if nodes \(i\) and \(j\) belong to the same community; otherwise, 0.