Low Earth Orbit (LEO) satellite constellations are becoming essential for expanding global Internet access, especially in remote and under-served areas. However, their highly dynamic nature, arising from network mobility, introduces complex coordination challenges between the dynamic satellites and the ground nodes (gateways and terrestrial devices). This is underscored by limited satellite visibility windows and spatially imbalanced user traffic demands. Local association (cell-satellite-gateway) strategies, such as nearest-satellite or greedy load-based selection, result in partial terrestrial coverage or lead to load imbalance that affects traffic demand fulfillment. Network-driven orchestration through centralized optimization can strike an efficient balance between these two key objectives, but is often computationally intensive for periodic operation and real-time deployment. This work presents a learning-based network orchestration framework, NEO-GNN, that models a satellite-ground network as a dynamic spatiotemporal graph. In contrast to prior works, it employs a heterogeneous Graph Neural Network (GNN), where satellites, gateways, and ground cells are modeled as distinct node types to capture their varied visibility and networking capabilities. They are trained in an unsupervised manner using tailored loss functions to balance the dual requirements of coverage and utilization, and produce efficient, real-time association decisions during inference. Evaluations show that NEO-GNN delivers complete ground-cell coverage, improves traffic demand satisfaction through balanced satellite and gateway use, and remains robust under dynamic visibility and partial satellite failures. NEO-GNN provides a scalable and efficient alternative to traditional optimization methods for real-time network orchestration in bent-pipe LEO satellite systems.

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