# Online Learning for Mixture of Multivariate Hawkes Processes

arXiv: https://arxiv.org/abs/2208.07961 · HTML: https://mlsanity.com/paper/2208.07961/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Learning Granger Causality for Hawkes Processes](https://mlsanity.com/paper/1602.04511/) (2016)
- [Lecture Notes: Temporal Point Processes and the Conditional Intensity Function](https://mlsanity.com/paper/1806.00221/) (2018)
- [Self-Attentive Hawkes Processes](https://mlsanity.com/paper/1907.07561/) (2019)
- [Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling](https://mlsanity.com/paper/1707.03003/) (2017)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [Shaping Social Activity by Incentivizing Users](https://mlsanity.com/paper/1408.0406/) (2014)
- [Tracking Dynamic Point Processes on Networks](https://mlsanity.com/paper/1409.0031/) (2014)

## Built on by (most cited first, 2 of 2)

- [What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes](https://mlsanity.com/paper/2406.03390/) (2024)
- [Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe](https://mlsanity.com/paper/2212.06081/) (2022)

## Abstract (arXiv)

Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either model the rich interaction between the events or the latent cluster of the actors or the network structure between the actors. We propose to model the latent structure of the network of actors as well as their rich interaction across events for real-world settings of medical and financial applications. Experimental results on both synthetic and real-world data showcase the efficacy of our approach.