## 1 Introduction
However, in real-world network traffic prediction scenarios, traditional models only focus on the changing trends of traffic volume while neglecting a crucial point: the data in network traffic does not exist in isolation, and there are complex topological relationships behind it. From the connection modes of network nodes to the data interactions between different links, these topological structures have a profound impact on the distribution and variation rules of traffic. Therefore, some research has achieved significant progress in this field by combining the graph convolutional network (GCN) [11] with time-series prediction models. The unique advantage of the GCN lies in its ability to incorporate these topological relationships into the model learning process. Through convolutional operations on graph-structured data, GCN can effectively explore the dependency relationships and feature propagation patterns among nodes in the network, enabling the model to learn the internal connections between traffic data from a more comprehensive perspective. As a result, it demonstrates a more excellent performance in network traffic prediction tasks, significantly improving the accuracy and reliability of prediction. The model proposed by the authors selects LSTM to handle the prediction of extremely large data volumes under surging business demands. Compared with using LSTM alone for network traffic prediction, the authors incorporated a GCN module as a spatial dependency extraction model. The introduction of GCN enables the model to fully exploit the complex relationships within the network topology, integrating the dependencies between nodes and traffic propagation patterns into the learning process. This allows for a more comprehensive understanding of the distribution patterns of traffic data. The combination not only enhances the model's ability to predict traffic changes, but also significantly improves its adaptability to bursty traffic and complex network environments, providing a more accurate and reliable solution for low Earth orbit (LEO) satellite constellation network traffic prediction. Communication between Low Earth Orbit (LEO) constellations is achieved through inter-satellite links, and the topological structure is shown in Figure 2. Satellites from satellite (1,1) to satellite (4,1) are co-orbital satellites, while those from satellite (1,1) to satellite (1,4) are cross-orbital satellites. Generally, as long as the communication equipment of the satellites is working properly and is not severely interfered with by the space environment, the links [12] between co-orbital satellites can usually remain connected, enabling stable data transmission and information exchange. For cross-orbital satellites, when they operate near the orbital intersection points, the distance between them is relatively short. If the communication equipment has the corresponding capabilities, a link can be established for communication at this time. Alternatively, by deploying relay satellites [13], signals can be forwarded between satellites in different orbits, realizing indirect communication between cross-orbital satellites and improving the possibility and stability of link connectivity. Due to differences in orbital altitude, inclination, etc., the distance and relative angle between cross-orbital satellites change significantly. At certain moments, the link may fail to be established or may be disconnected because the distance is too far or the angle is unfavorable, exceeding the coverage and capabilities of the communication equipment. Therefore, the time-series prediction model incorporating GCN is highly suitable for LEO constellation scenarios. Due to the special characteristics of satellite networks, including high dynamics, complex network topologies, and diverse connections between different satellites, these factors allow the GCN to fully leverage its own advantages.