Situational awareness on a graph: towards graph neural networks for spectrum analysis and battlefield management

Graph Neural Networks (GNN) were originally developed to infer relationships between objects in complex graph environments such as social networks. However, they have recently been applied to other domains which naturally support graph expression, such as hardware and software analysis. We propose to extend the application of GNNs to datasets which contain a temporal component, thus enabling GNN inference of event-driven situations involving the radio frequency (RF) spectrum. Post-battle analysis can train a GNN to identify individual subgraphs representing sequences of events. Trained GNNs can then be used in war time to infer a larger situation as a series of subgraphs are identified.