Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolving-Graph Gaussian Processes (e-GGPs). The proposed method is capable of learning the transition function of graph vertices over time with a neighbourhood kernel to model the connectivity and interaction changes between vertices. We assess the performance of our method on time-series regression problems where graphs evolve over time. We demonstrate the benefits of e-GGPs over static graph Gaussian Process approaches.

翻译：图形 Gaussian 进程( GGPs) 提供了图形结构化域的数据效率解决方案。 现有方法侧重于静态结构, 而许多真实的图形数据代表了动态结构, 限制了GGPs的应用。 要克服这一点, 我们建议了正在演变的Graph Gaussian进程( e- GGPs)。 拟议的方法能够用邻里内核来学习图形顶端的过渡功能, 以模拟顶端之间的连接和互动变化。 我们评估了我们处理时间序列回归问题的方法的性能, 即图表随时间演变而演变。 我们展示了电子GGPs相对于静态图形高斯进程方法的效益 。