This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and their blending induces intractable complexity in the temporal evolution over graphs. Drawing inspiration from the recent process of physical dynamic models in deep neural networks, we propose Graph Neural Controlled Differential Equation (GN-CDE) model, a generic differential model for dynamic graphs that characterise the continuously dynamic evolution of node embedding trajectories with a neural network parameterised vector field and the derivatives of interactions w.r.t. time. Our framework exhibits several desirable characteristics, including the ability to express dynamics on evolving graphs without integration by segments, the capability to calibrate trajectories with subsequent data, and robustness to missing observations. Empirical evaluation on a range of dynamic graph representation learning tasks demonstrates the superiority of our proposed approach compared to the baselines.

翻译：本文聚焦于具有时间交互的动态图的表示学习。一个基本问题在于，图结构和节点本身均具有各自的动态性，二者的融合导致图的时间演化产生难以处理的复杂性。受近期深度神经网络中物理动态模型进展的启发，我们提出了图神经控制微分方程（GN-CDE）模型，这是一种通用的动态图微分模型，它通过神经网络参数化的向量场以及交互对时间的导数，刻画了节点嵌入轨迹的持续动态演化过程。我们的框架展现出若干理想特性，包括无需分段积分即可表达演化图上的动态性、能够利用后续数据校准轨迹，以及对缺失观测的鲁棒性。在一系列动态图表示学习任务上的实验评估表明，所提方法相对于基线具有优越性。