## 1 Introduction  
The aviation network functions as a typical complex system, with its subsystems that interact to generate functionality rather than being created by a specific subsystem [1, 2, 3]. Complex network theory enhances our understanding of the functionality and behavior of the aviation transportation system. The aviation network consists of nodes and edges, with nodes that represent airports or cities where airports are located and edges that represent connections between airports [4]. To model the US aviation network, [PERSON] [5] utilized a directed weighted network and analyzed its degree distribution and clustering coefficient. [PERSON] and [PERSON] [6] analyzed the Chinese aviation network (CAN) with 128 airport nodes and 1165 flights, and found that its average shortest path was 2.067 and its clustering coefficient was 0.733, indicative of a small-world network classification. [PERSON] [7] examined the global aviation-weighted network and investigated the correlation between weight and the underlying topological structures of the network. [PERSON] [8] analyzed the interaction between macroscopic and microscopic airport network characteristics in the US. Empirical studies demonstrate that both Chinese and global aviation networks have small-world properties, with short average shortest paths and high clustering coefficients. Both networks exhibit a double power-law distribution pattern in their respective degree distributions [9; 10; 11; 12]. The majority of studies on aviation networks have concentrated on examining their static structures, overlooking the correlations present across various time periods. In static networks, the relationship between nodes depends only on the existence of a flight route, which disregards any temporal data such as departure and arrival times or delays. This oversimplified static abstraction leads to overestimating the connections among nodes and distorts the understanding of the system's structure. Temporal networks are characterized by the connections among nodes which vary over time periods [13]. Integration of the time dimension into the network model results in more complex interrelationships and presents novel challenges to static network theory. Early researchers described temporal networks using weighted networks, with edge weight signifying the number of incidences over the entire time period. Nevertheless, this model considers an impractical assumption that the connections between pairs of nodes are randomly distributed within the time period [14]. To evaluate the presence of directed edges between nodes and to establish reachability graphs, [PERSON] et al. [15] introduced the concept of temporal reachability. However, this model is only suitable for sparse networks. To overcome this drawback, several studies [16; 17] discretized aggregated temporal networks into snapshot sequences using time windows, where the interactions between nodes within a time window belong to the same time window, and each snapshot within the time window is independent of the others. By correctly mapping temporal networks into static networks, the research characterized changes in network features such as the degree, betweenness, and clustering coefficient. Although these methods are broadly accepted in temporal network research, the selection of an appropriate time window size remains a crucial challenge. A small time window setting results in numerous invalid empty network structures, whereas using the entire observation window as the time window renders the temporal network static, leading to the loss of temporal information. Furthermore, these models ignore continuity in time when the interaction between nodes spans multiple time windows, resulting in inaccurate temporal information. In recent years, there has been a growing trend among scholars to utilize temporal networks for the aviation transportation network. [PERSON] et al.