Ordinal Historical Dependence in Graphical Event Models with Tree Representations


  • Debarun Bhattacharjya IBM Research
  • Tian Gao IBM Research
  • Dharmashankar Subramanian IBM Research


Probabilistic Graphical Models, Mining of Spatial, Temporal or Spatio-Temporal Da, Graphical Models


Graphical event models are representations that capture process independence between different types of events in multivariate temporal point processes. The literature consists of various parametric models and approaches to learn them from multivariate event stream data. Since these models are interpretable, they are often able to provide beneficial insights about event dynamics. In this paper, we show how to compactly model the situation where the order of occurrences of an event’s causes in some recent historical time interval impacts its occurrence rate; this sort of historical dependence is common in several real-world applications. To overcome the practical challenge of parameter explosion due to the number of potential orders that is super-exponential in the number of parents, we introduce a novel graphical event model based on a parametric tree representation for capturing ordinal historical dependence. We present an approach to learn such a model from data, demonstrating that the proposed model fits several real-world datasets better than relevant baselines. We also showcase the potential advantages of such a model to an analyst during the process of knowledge discovery.




How to Cite

Bhattacharjya, D., Gao, T., & Subramanian, D. (2021). Ordinal Historical Dependence in Graphical Event Models with Tree Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6759-6767. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16835



AAAI Technical Track on Machine Learning I