Space-Independent Community Detection in Airport Networks

This research explores the topology and passenger flows of the United States Airport Network (USAN) over two decades. The network model consists of a time-series of six network snapshots for the years 1990, 2000 and 2010, which capture bi-monthly passenger flows among US airports. Since the network is embedded in space, the volume of these flows is naturally affected by spatial proximity, and therefore, a model (recently proposed in the literature) accounting for this phenomenon is used to identify the communities of airports that have particularly high flows among them, given their spatial separation. This research results highlight the fact that some general techniques from network theory, such as network modelling and analysis, can be successfully applied for the study of a wide range of complex systems, while others, such as community detection, need to be tailored for a specific system.

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