Point process models are widely used to analyze asynchronous events occurring
within a graph that reflect how different types of events influence one
another. Predicting future events' times and types is a crucial task, and the
size and topology of the graph add to the challenge of the problem. Recent
neural point process models unveil the possibility of capturing intricate
inter-event-category dependencies. However, such methods utilize an unfiltered
history of events, including all event categories in the intensity computation
for each target event type. In this work, we propose a graph point process
method where event interactions occur based on a latent graph topology. The
corresponding undirected graph has nodes representing event categories and
edges indicating potential contribution relationships. We then develop a novel
deep graph kernel to characterize the triggering and inhibiting effects between
events. The intrinsic influence structures are incorporated via the graph
neural network (GNN) model used to represent the learnable kernel. The
computational efficiency of the GNN approach allows our model to scale to large
graphs. Comprehensive experiments on synthetic and real-world data show the
superior performance of our approach against the state-of-the-art methods in
predicting future events and uncovering the relational structure among data.

本研究提出了基于图点过程的方法来预测未来的事件，并利用图神经网络模型来表示可学习核，实现了对事件之间的关系结构的揭示，该方法在预测未来事件和展示数据关系结构方面优于现有方法。