  Learning node representation on dynamically-evolving, multi-relational graph
data has gained great research interest. However, most of the existing models
for temporal knowledge graph forecasting use Recurrent Neural Network (RNN)
with discrete depth to capture temporal information, while time is a continuous
variable. Inspired by Neural Ordinary Differential Equation (NODE), we extend
the idea of continuum-depth models to time-evolving multi-relational graph
data, and propose a novel Temporal Knowledge Graph Forecasting model with NODE.
Our model captures temporal information through NODE and structural information
through a Graph Neural Network (GNN). Thus, our graph ODE model achieves a
continuous model in time and efficiently learns node representation for future
prediction. We evaluate our model on six temporal knowledge graph datasets by
performing link forecasting. Experiment results show the superiority of our
model.
