  Space-time graph neural networks (ST-GNNs) are recently developed
architectures that learn efficient graph representations of time-varying data.
ST-GNNs are particularly useful in multi-agent systems, due to their stability
properties and their ability to respect communication delays between the
agents. In this paper we revisit the stability properties of ST-GNNs and prove
that they are stable to stochastic graph perturbations. Our analysis suggests
that ST-GNNs are suitable for transfer learning on time-varying graphs and
enables the design of generalized convolutional architectures that jointly
process time-varying graphs and time-varying signals. Numerical experiments on
decentralized control systems validate our theoretical results and showcase the
benefits of traditional and generalized ST-GNN architectures.

Machine Learning;Signal Processing