  The impact of air transport delays and their propagation has long been
studied, mainly from environmental and mobility viewpoints, using a wide range
of data analysis tools and simulations. Less attention has nevertheless been
devoted to how delays create meso-scale structures around each airport. In this
work we tackle this issue by reconstructing functional networks of delay
propagation centred at each airport, and studying their identifiability (i.e.
how unique they are) using Deep Learning models. We find that such delay
propagation neighbourhoods are highly unique when they correspond to airports
with a high share of Low Cost Carriers operations; and demonstrate the
robustness of these findings for the EU and US systems, and to different
methodological choices. We further discuss some operational implications of
this uniqueness.
