Fig 1.
Frequency distributions of degree (a), betweenness (b), and closeness centrality (c) of the Chinese high-speed rail network.
All metrics are plotted on a logarithmic scale. The system can be divided into two regimes (red vs. green) using a cut-off at the local minima of the probability density curves (a-c) calculated using ksdensity function in Matlab 2011b. The high- and low-centrality stations are placed on the map, as represented by red and green dots, respectively (d-f).
Fig 2.
Venn diagrams for the high- and low-centrality stations identified for the three network metrics.
Each colored circle representing a high- or low-centrality station set is identified from the frequency distribution of a given network metric (see Fig 1). Overlapping regions of circles indicate the number (proportion in the brackets) of the identical stations identified by corresponding metrics.
Fig 3.
Pairwise relationships between the three network metrics (a-c).
The purple-point groups deviating from the main cluster are identical in (b) and (c), representing stations (purple dots) that are isolated from the main body (gray dots) of the network (d). Univariate linear regression with the ordinary least square method shows that all pairwise relationships are significant at the level of P<0.001. The purple dots as deviants are excluded from the regression analysis in (b) and (c). All metrics are log-transformed and then normalized to 0–1.