External (weather or ground operations) and internal (passenger connections and aircraft rotations) uncertainties affecting airline operators drive flight delays and generate disrupted operations as a consequence. These disruptions can be mitigated proactively if accurate predictions of events such as flight arrivals are available. This paper investigates the flight arrival delay prediction problem from an airline-centered perspective, considering both external factors for flight delays and internal factors for delay propagation. This paper proposes a network-aware Graph Neural Network trained on discrete-time snapshots of a hub airline network. The model considers aircraft rotations and passenger connections as independent delay propagation sources between flights, incorporating them into flight arrival delay prediction. The data set used for the case study concerns SWISS International Airlines and Edelweiss flights in 2025, with a Zurich-hub emphasis. Two Gradient Boosted Decision Trees (GBDT) baselines (with and without explicit graph features) are designed to assess the performance of the proposed Discrete-Time Heterogeneous Graph Neural Network. The main findings of the work demonstrate the benefits of dynamic network-aware models, offering more than 1 minute of lower mean absolute error than proposed baselines. Furthermore, operationally relevant features absent from previous work, including weather conditions and airport congestion, are shown to improve predictions across both model families. Finally, it is shown that the model reports decreasing performance with respect to the time horizon, but implicitly learns operational constraints such as the night curfew, where prediction accuracy recovers relative to other periods in the second half of the day. The presented model can be used as an input for more accurate early operational decisions, such as early re-booking or identification of critical connecting passengers. ... Read more