Space-Time Graph Neural Networks with Stochastic Graph Perturbations

In this paper, we bridge this gap and Space-time graph neural networks (ST-GNNs) are recently developed study both signals and graphs that vary over time. We extend our architectures that learn efficient graph representations of timevarying previous work in ST-GNNs to accommodate time-varying graphs data. ST-GNNs are particularly useful in multi-agent systems, and prove their stability to stochastic graph perturbations. Our contributions due to their stability properties and their ability to respect communication can be summarized as follows: delays between the agents. In this paper we revisit the stability properties of ST-GNNs and prove that they are stable to (C1) We prove the stability of STGFs and ST-GNNs to stochastic stochastic graph perturbations. Our analysis suggests that ST-GNNs graph perturbations. Our result implies that ST-GNNs can are suitable for transfer learning on time-varying graphs and enables handle transfer learning to time-varying graphs.