  There has been an increasing interest in inferring future links on temporal
knowledge graphs (KG). While links on temporal KGs vary continuously over time,
the existing approaches model the temporal KGs in discrete state spaces. To
this end, we propose a novel continuum model by extending the idea of neural
ordinary differential equations (ODEs) to multi-relational graph convolutional
networks. The proposed model preserves the continuous nature of dynamic
multi-relational graph data and encodes both temporal and structural
information into continuous-time dynamic embeddings. In addition, a novel graph
transition layer is applied to capture the transitions on the dynamic graph,
i.e., edge formation and dissolution. We perform extensive experiments on five
benchmark datasets for temporal KG reasoning, showing our model's superior
performance on the future link forecasting task.
