## 4 Evaluation Indicators  
Given the complexity and diversity of evaluation factors, the impact of different evaluation indicators on the evaluation results is also different; therefore, the reasonable selection and use of evaluation indicators are crucial for obtaining scientific evaluation results. Considering the advantages and disadvantages of different evaluation indicators, based on references [25; 27], this study selected six classic evaluation indicators from global  
Figure 5: Topological map of the air transportation network. Figure 6: Topological map of the water transportation network. and local perspectives, including closeness centrality, degree centrality, network constraint coefficient, betweenness centrality, clustering coefficient, and eigenvector centrality. The following are the six evaluation indicators:  
(1) Degree centrality  
The centrality indicator degree centrality (\(DC_{i}\)) measures the node's influence, which is an essential parameter for studying the topology of complex networks. The number of the connection of individual node \(i\) is denoted as \(d_{i}\), that is, the number of links connected between node \(i\) and other nodes within the entire network. The maximum number of possible connections within the entire network is \(n-1\). The degree centrality indicates the ratio of \(d_{i}\) to \(n-1\), as shown in Equation (2) [19; 33]. \[DC_{i}=\frac{d_{i}}{n-1}, \tag{2}\]  
where \(n\) is the number of nodes in the network. Generally, a node with a higher \(DC_{i}\) is directly connected to more nodes, thus occupying a more important position within the network. (2) Betweenness centrality (\(BC_{i}\))  
\(BC_{i}\) is the number of the shortest paths between the node and node \(d\) passing through an intermediate node \(i\)[13]. \(BC_{i}\) can be obtained from Equation (3):  
\[BC_{i}=\frac{2}{n^{2}-3n+2}\sum_{s<d}\frac{g_{sd(i)}}{g_{sd}}, \tag{3}\]  
where \(g_{sd}\) denotes the total number of shortest paths between node \(s\) and node \(d\), and \(g_{sd(i)}\) indicates the number of shortest paths between node \(s\) and \(d\) passing through node \(i\). The higher the \(BC_{i}\) is, the more important vertex \(i\) is. (3) Closeness centrality (\(CC_{i}\))  
\(CC_{i}\) represents the reciprocal of the average distances of the shortest path between node \(i\) and all other nodes [33; 35], which is defined as follows:  
\[CC_{i}=\frac{n-1}{\sum_{j=1}^{n}d_{ij}}, \tag{4}\]  
where \(d_{ij}\) is the minimum distance between node \(i\) and node \(j\). The closer node \(i\) is to the network center, the greater \(CC_{i}\) becomes. (4) Eigenvector centrality (\(EC_{i}\))  
\(EC_{i}\) indicates the linear relationship between the centrality indicator of vertex \(i\) and the centrality metrics of the neighboring nodes [33], which is defined as follows:  
\[EC_{i}=\frac{1}{\theta}\sum_{j=1}^{n}a_{ij}EC_{j}, \tag{5}\]  
where proportionality constant \(\theta\) is the maximum eigenvalue of the \(n\)th-order adjacent matrix A = (\(a_{ij}\)). \(EC_{j}=\left[EC_{1}EC_{2}EC_{3}\cdot\cdot\cdot\cdot\cdot EC_{n}\right]^{T}\) denotes the center vector of all vertices. The larger \(EC_{i}\) is, the more import node \(i\) is. (5) Clustering coefficient (\(CLC_{i}\))  
\(CLC_{i}\) reflects the tendency of node \(i\) to form clusters with its neighboring nodes, as shown in Equation (6). In general, only nodes with relatively low clustering coefficient values can become structural hole nodes [25]. \[CLC_{i}=\frac{2e(i)}{d_{i}[d_{i}-1]}, \tag{6}\]where \(e(i)\) denotes the links number from node \(i\) to its neighboring nodes. \(d_{i}\) is the degree of node \(i\). (6) Network constraint coefficient (\(RC_{i}\))  
[PERSON] put forward structural hole theory in 1992. The network constraint coefficient is an indicator used by [PERSON] to reflect the number of structural holes and the extent of their impact on the information flow [36].