This repo includes the Python implementation of the CHIP network model as well as the code to replicate all experiments in the paper CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation, presented at Neural Information Processing Systems (NeurIPS) 2020.

CHIP is a generative model for continuous-time networks of timestamped relational events, where each event is a triplet (i, j, t) denoting events from node i (sender) to node j (receiver) at timestamp t.

Main contributions:

1. We demonstrate that spectral clustering provides consistent community detection in CHIP for a growing number of nodes.
2. We propose consistent and computationally efficient estimators for the model parameters for a growing number of nodes and time duration.
3. We show that CHIP provides better fits to several real datasets and scales to much larger networks than existing models, including a Facebook network with over 40,000 nodes and over 800,000 events.

This repo has been developed and tested using Python 3.6.9. The code does not work with Python 2.7.

To run experiments, either clone or fork this repository and refer to requirements.txt for the required packages.

All datasets used in this repo are either available in the storage/datasets directory or will be automatically downloaded by the preprocessing script.

There are 3 Jupyter notebook examples of the CHIP model in the examples directory:

• generating_chip_networks.ipynb: how to generate and fit networks using CHIP
• facebook_wallposts_exploratory_analysis.ipynb: fit CHIP to the largest connected component of the Facebook Wall Post dataset
• enron_exploratory_analysis.ipynb: fit CHIP to the Enron dataset

Please contact us if you have any questions or to report an issue. You can find the contact information of all three authors in the paper.

This repository has been published for the sole purpose of providing more information on the aforementioned publication.