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.

翻译：点过程模型被广泛用于分析图中发生的异步事件，这些事件反映了不同类型事件之间的相互影响。预测未来事件的时间和类型是一项关键任务，而图的大小和拓扑结构增加了问题的复杂性。最新的神经点过程模型揭示了捕捉事件类别间复杂依赖关系的可能性。然而，此类方法在计算每个目标事件类型的强度时，使用了包含所有事件类别的未过滤历史事件。本文提出了一种基于潜在图拓扑结构进行事件交互的图点过程方法。相应的无向图中，节点表示事件类别，边表示潜在的贡献关系。我们进一步开发了一种新型深度图核函数，用以刻画事件之间的激发与抑制效应。通过图神经网络模型表示可学习核函数，从而融入内在影响结构。图神经网络方法的计算高效性使模型能够扩展到大规模图。在合成数据和真实数据上的综合实验表明，我们的方法在预测未来事件和揭示数据间关系结构方面优于现有最先进方法。