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.

本研究提出一种基于神经常微分方程的多关系图卷积网络连续模型，通过图转换层捕获动态图的转换，实现对时间和结构信息进行连续时间动态嵌入，并在五个基准数据集上进行了大量实验证明，该模型在未来链接预测任务中具有卓越的性能。