Cardinality-Regularized Hawkes-Granger Model

Tsuyoshi (Ide-san) Ide

IBM Thomas J. Research Center, tide@us.ibm.com / ide@ide-research.net

What is this repository about?

This repository provides an end-to-end demo of a Granger-causal analysis approach proposed in

Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe, "Cardinality-Regularized Hawkes-Granger Model," Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (slides).

This Jupyter notebook provides full details (tested on 06/10/2024 on a clean pip environment with libraries described in requirements.txt). Enjoy!

Dependency

The core module L0HawkesLib.py depends only on numpy. This is the only critical dependency of this Python implementation of the L0Hawkes algorithm.

For visualization purposes, a utility library called L0HawkesVisualize.py has been included, which depends on:

• pandas
• matplotlib
• seaborn

If you want to reproduce the notebook as it is, I suggest running

pip install numpy pandas matplotlib seaborn

on a clean pip environment so all the sub-libraries will be automatically installed.

If you are not comfortable with installing these, just download only L0HawkesLib.py and use it in your environment.