Efficient Extrapolation-Based Dynamic Link Prediction for Large-Scale Satellite Networks

Abstract

Inter-satellite links (ISLs) in large LEO constellations vary quickly with time, and their rapid change makes future link-state prediction a practical concern. Predicting ISLs with reasonable accuracy benefits routing decisions, resource scheduling, and overall network stability. Existing methods are still limited in several ways. Rule-based and physics-based methods build on hand-crafted assumptions, which become unreliable once links flicker frequently or topologies grow heterogeneous. Static graph neural networks describe the relations within a single snapshot, but their formulation does not represent how ISLs evolve from one moment to the next. Many recent dynamic graph models stay close to interpolation, and they exploit physical and temporal satellite attributes only in part, which weakens both node representations and prediction accuracy. To address these gaps, we develop an extrapolation-oriented dynamic link prediction framework with three contributions: (1) we shift the task from interpolation toward future-state extrapolation, which makes proactive management possible; (2) we extract physics- and temporal-aware node features by combining orbital dynamics with historical ISL information; (3) we restrict prediction to a candidate edge set, so the computation stays manageable while the method remains scalable. In experiments, the proposed method outperforms existing baselines on accuracy, efficiency, and scalability, suggesting that it can serve as a useful tool for managing large-scale LEO networks in practice.