Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.

翻译：神经时序点过程（TPPs）在连续时间事件序列建模中展现出良好的潜力，然而捕捉事件之间的交互关系仍是实现事件序列预测等推理任务的关键挑战。现有TPP模型主要集中于参数化未来事件的条件分布，但在建模事件交互方面存在不足。本文提出了一种基于神经关系推理（NRI）的新方法，该方法能够从观测数据中同时学习动态模式与交互关系，并通过构建关系图实现推理。我们的模型——对比关系推理霍克斯过程（CRIHP）——在变分推理框架下推理事件交互，利用基于强度的学习方法搜索原型路径来对比关系约束。在三个真实世界数据集上的大量实验表明，该模型在事件序列建模任务中能够有效捕获事件交互关系。