Topics related to community detection and complex network indicators are increasing notoriety in the literature. In this research, we focus on network indicators such as average degree, average clustering coefficient, average path length, network density, network diameter, nodes centrality, and on detecting communities with the Louvain algorithm. We build an European airports network and apply and discuss the network indicators, as well as the community detection algorithm. The primary aim of the research is to analyze several network indicators and to check how the split of the network in communities is changing the range of values for the respective network indicators when computed at the community level. The paper addresses a novel approach in the field of air transportation, firstly by representing 50 European airports and the flights operated with the help of a network, and then by illustrating how community detection can be applied to identify relevant groups of airports.