## 1 Introduction
[PERSON] and associates [11] have crafted a dynamic ripple delay propagation model that takes into account the variability in weather conditions and geographical disparities. This model excels at depicting the propagation of flight delays under the strain of adverse weather, providing a more nuanced understanding of the complexities involved. Building upon the cumulative research of these scholars, this paper integrates their findings and introduces a novel approach. It utilizes operational index data from major airports, navigation points, and routes in East China as the basis for network edge weights. By establishing a weighted route network model, we employ the analytical methods of complex network theory to delve into the structural characteristics and performance of the East China route network. This comprehensive analysis aims to provide a more robust framework for understanding and mitigating flight delays within the region. Having delved into the intricacies of constructing a route network model, we now shift our focus to a closely intertwined concern: the selection of an appropriate propagation dynamics model. The route network model lays the groundwork by mapping the interconnections among nodes, represented by airports. However, it is the propagation dynamics model that delves deeper into the heart of the matter, clarifying the underlying principles that govern the spread of flight delays. Choosing the right dynamics model is paramount for achieving an accurate representation and forecast of flight delays. It is this model that captures the nuances of how delays ripple through the network, affecting various nodes in a cascading manner. The selection process must consider the model's ability to reflect the complexity of real-world flight operations, including the impact of external factors such as weather conditions, mechanical issues, and air traffic congestion. In essence, the propagation dynamics model serves as the linchpin that translates the structural insights gained from the route network model into actionable predictions. It is through this model that we can simulate various scenarios, assess the vulnerability of different nodes, and devise strategies to mitigate the propagation of delays. The accuracy and reliability of this model are therefore critical to the effectiveness of any intervention aimed at enhancing the resilience and efficiency of the aviation network. In the quest to understand the complexities of airline route networks, [PERSON] and colleagues [12] have focused on developing a route time series network model. This innovative model is designed to analyze and predict time series data, offering a dynamic perspective on the evolution and patterns of flight data over time. By capturing the temporal fluctuations, this model enhances our ability to assess the robustness of the network against disruptions. [PERSON] and team [13] have introduced a multilayer network model with active nodes, a significant advancement in the field of complex network theory. This model adeptly describes intricate systems that consist of multiple layers of interconnected networks. The incorporation of active nodes endows the multilayer network with time-varying characteristics, effectively simulating real-world scenarios that include latency periods and the intriguing phenomenon of nodes becoming susceptible again after recovery, thus providing a more realistic representation of network dynamics. [PERSON] and associates [14] have proposed a coupled diffusion model that is instrumental in examining the propagation of information or states through both inter-layer and intra-layer connections within multilayer networks. Their research delves into the competitive dynamics between accurate and inaccurate messages and how this competition influences the spread of infections, offering valuable insights into the control of misinformation in networked systems. [PERSON] and co-researchers [15] have constructed a symbiotic evolution model, building upon the well-established SIR (Susceptible-Infected-Recovered) model.