Notebooks and data used in the upcoming paper "Detecting communities in higher-order networks by using their derivative graphs".
This repository is structured as follows:
- Notebooks (.ipynb): One per simulation - Toy Model, Primary School and Coauthor networks. Most relevant information and example usage can be found here.
- hyperfunctions.py, modularity_functions.py: Sets of functions called in the notebooks.
- CollaborationNetwork-????.py: Statistics of time taken by the three algorithms (???? = General, ModUs, ModKumar) can be obtained with these scripts.
- Figures: Outputs of the notebooks.
- Datasets: Folder containing the .csv files of the Coauthor and Primary School networks.