Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization

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 estimationthis http URLthe 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 computationalthis http URLTPPs 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.

@article{wang2025_2503.23002,
  title={ Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization },
  author={ Qingmei Wang and Fanmeng Wang and Bing Su and Hongteng Xu },
  journal={arXiv preprint arXiv:2503.23002},
  year={ 2025 }
}