  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.
