Statistical Analysis of Airport Network of China

TL;DR

This paper analyzes the topological and statistical properties of China's airport network, revealing small-world characteristics, power-law degree distributions, and relationships between flight weights and airport connectivity.

Contribution

It provides a comprehensive statistical and topological analysis of China's airport network, highlighting its small-world features and power-law distributions, which were not previously characterized in detail.

Findings

The network exhibits small-world properties with short average path length and high clustering.

Degree distributions follow a double Pareto law with different exponents.

Flight weights have power-law tails and are proportional to airport degrees.

Abstract

Through the study of airport network of China (ANC), composed of 128 airports (nodes) and 1165 flights (edges), we show the topological structure of ANC conveys two characteristics of small worlds, a short average path length (2.067) and a high degree of clustering (0.733). The cumulative degree distributions of both directed and undirected ANC obey two-regime power laws with different exponents, i.e., the so-called Double Pareto Law. In-degrees and out-degrees of each airport have positive correlations, whereas the undirected degrees of adjacent airports have significant linear anticorrelations. It is demonstrated both weekly and daily cumulative distributions of flight weights (frequencies) of ANC have power-law tails. Besides, the weight of any given flight is proportional to the degrees of both airports at the two ends of that flight. It is also shown the diameter of each sub-cluster (consisting of an airport and all those airports to which it is linked) is inversely proportional to its density of connectivity. Efficiency of ANC and of its sub-clusters are measured through a simple definition. In terms of that, the efficiency of ANC's sub-clusters increases as the density of connectivity does. ANC is found to have an efficiency of 0.484.