The Hawkes process has become a standard method for modeling self-exciting
event sequences with different event types. A recent work has generalized the
Hawkes process to a neurally self-modulating multivariate point process, which
enables the capturing of more complex and realistic impacts of past events on
future events. However, this approach is limited by the number of possible
event types, making it impossible to model the dynamics of evolving graph
sequences, where each possible link between two nodes can be considered as an
event type. The number of event types increases even further when links are
directional and labeled. To address this issue, we propose the Graph Hawkes
Neural Network that can capture the dynamics of evolving graph sequences and
can predict the occurrence of a fact in a future time instance. Extensive
experiments on large-scale temporal multi-relational databases, such as
temporal knowledge graphs, demonstrate the effectiveness of our approach.

作者们提出了一种名为 Graph Hawkes Neural Network 的方法，可以用来建模动态图序列中的事件发生，同时还可以预测未来事件发生的时间和类型。实验证明，该方法对于大规模的时态多关系数据库非常有效，例如时间知识图谱。