MHP

This repository contains a light-weight python implementation for generating events and estimating the parameters using a Multivariate Hawkes Process. It contains the following code components:

• HP/simulators: To generate a cascade with given parameters using the modified Ogata's thinning algorithm.
• HP/estimators: To estimate the parameters of multivariate hawkes process using maximum likelihood.

The simulation code is copied and modified slightly from Steve Morse's excellent implementation. The code is optimized to get runtime improvements compared to naive implementations.

If you happen to use this code, consider citing our paper. Here's the open-access version

@inproceedings{soni2019detecting,
  title={Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers},
  author={Soni, Sandeep and Ramirez, Shawn Ling and Eisenstein, Jacob},
  booktitle={The 2019 ACM SIGKDD Workshop on Causal Discovery},
  pages={78--99},
  year={2019}
}

If you discover any bug, please file an issue. For contributions, please make a pull request.

TODO

• Add Jupyter notebook demostrating how to use both the generation and estimation code

Other similar resources

• Scott Linderman provides code for bayesian inference on Hawkes processes.
• The Hawkes Process Toolkit by Hongteng Xu and Hongyuan Zha is a comprehensive toolkit for doing estimation and simulation using Hawkes process. It is writte in Matlab.