Space-Time Graph Neural Networks with Stochastic Graph Perturbations

• URL: http://arxiv.org/abs/2210.16270v1
• Date: Fri, 28 Oct 2022 16:59:51 GMT
• Title: Space-Time Graph Neural Networks with Stochastic Graph Perturbations
• Authors: Samar Hadou, Charilaos Kanatsoulis, and Alejandro Ribeiro
• Abstract summary: Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
• Score: 100.31591011966603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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