Exploration of the global air transport network using social network analysis

Air travel has now become one of the most commonly used modes of transportation across the world due to its ease of access, faster commute, and reasonable costs. Its increasing demand has made it possible to achieve connectivity to nearly every part of the world, with a growing number of direct flights to major cities. Studying the network of flight routes through social network analysis (SNA) helps us determine the airports that are significant players in the industry. By calculating the clustering coefficient and the average shortest path, we can ascertain that the world airport network (WAN) has the characteristics of a small-world network. In contrast, some regional networks possessed features of both small-world and scale-free networks. Previous studies conducted have primarily focused on complex air networks in a particular region. What sets our study apart is the use of a large dataset to analyse the properties of air transport across various parts of the world. Our aim through this project was to better understand the characteristics and patterns of air transport around the world. We used various measures of SNA to arrive at our output, which included a comparison of regional airport networks, their importance in the network, and influence airports have on WAN. The tools used for analysis were designed with Python and the network handling package Networkx.