Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization
This addresses the need for more interpretable TPP models in applications like event sequence analysis, though it is incremental as it builds on existing TPP frameworks with a novel regularization approach.
The paper tackles the problem of temporal point processes (TPPs) ignoring clustering structures in event sequences, which reduces interpretability, by introducing Gromov-Wasserstein regularization to impose clustering on sequence embeddings, resulting in competitive predictive and clustering performance without compromising accuracy.
Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we learn structure-enhanced TPPs with the help of Gromov-Wasserstein (GW) regularization, which imposes clustering structures on the sequence-level embeddings of the TPPs in the maximum likelihood estimation framework.In the training phase, the proposed method leverages a nonparametric TPP kernel to regularize the similarity matrix derived based on the sequence embeddings. In large-scale applications, we sample the kernel matrix and implement the regularization as a Gromov-Wasserstein (GW) discrepancy term, which achieves a trade-off between regularity and computational efficiency.The TPPs learned through this method result in clustered sequence embeddings and demonstrate competitive predictive and clustering performance, significantly improving the model interpretability without compromising prediction accuracy.