Proceedings

Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph

Yunhao Zhang, Junchi Yan

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3406-3412.

https://doi.org/10.24963/ijcai.2021/469

Keywords

  • Machine Learning: Time-series; Data Streams
  • Machine Learning: Relational Learning

Abstract

Relation discovery for multi-dimensional temporal point processes (MTPP) has received increasing interest for its importance in prediction and interpretability of the underlying dynamics. Traditional statistical MTPP models like Hawkes Process have difficulty in capturing complex relation due to their limited parametric form of the intensity function. While recent neural-network-based models suffer poor interpretability. In this paper, we propose a neural relation inference model namely TPP-NRI. Given MTPP data, it adopts a variational inference framework to model the posterior relation of MTPP data for probabilistic estimation. Specifically, assuming the prior of the relation is known, the conditional probability of the MTPP conditional on a sampled relation is captured by a message passing graph neural network (GNN) based MTPP model. A variational distribution is introduced to approximate the true posterior. Experiments on synthetic and real-world data show that our model outperforms baseline methods on both inference capability and scalability for high-dimensional data.

Yunhao Zhang, and Junchi Yan. 2021. Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3406–3412. https://doi.org/10.24963/ijcai.2021/469
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