论文标题
带有随机图扰动的时空图神经网络
Space-Time Graph Neural Networks with Stochastic Graph Perturbations
论文作者
论文摘要
时空图神经网络（ST-GNNS）是最近开发的架构，可以学习有效的时间变化数据的图形表示。 ST-GNNS在多代理系统中特别有用，因为它们的稳定性及其尊重代理之间的通信延迟的能力。在本文中，我们重新审视了ST-GNNS的稳定性，并证明它们在随机图扰动中稳定。我们的分析表明，ST-GNN适用于随着时间变化的图表的转移学习，并可以设计一般的卷积体系结构，共同处理时间变化的图形和随时间变化的信号。分散控制系统的数值实验验证了我们的理论结果，并展示了传统和广义的ST-GNN架构的好处。
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.
