  Asynchronous time series, also known as temporal event sequences, are the
basis of many applications throughout different industries. Temporal point
processes(TPPs) are the standard method for modeling such data. Existing TPP
models have focused on parameterizing the conditional distribution of future
events instead of explicitly modeling event interactions, imposing challenges
for event predictions. 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. Code will be
integrated into the EasyTPP framework.
