Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations. To address this limitation, we introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.

翻译：现代图表示学习工作大多假设处理的是均匀采样的时间图快照，这远不符合现实情况——例如，社交网络和物理系统具有连续动态和零星观测的特征。为弥补这一局限，我们提出了时间图常微分方程（TG-ODE）框架，该框架能从观测间隔不均匀的图流中同时学习时间与空间动态。我们在多个图基准上对提出的方法进行了实证验证，结果表明TG-ODE能在非均匀图流任务中达到当前最佳性能。