XOR: community detection and ranking extraction in networks

Python implementation of XOR algorithm described in:

• [1] Iacovissi L. and De Bacco C. (2022). The interplay between ranking and communities in networks, Sci Rep 12, 8992.

If you use this code please cite this article (published version, open access) or this article (preprint).

We present a new generative model based on an interplay between the community and hierarchical structures. It assumes each node to have a preference between the homophily-based (community) and the competition-based (hierarchy) interaction mechanisms. Nodes with the same predilection are more likely to interact, while heterogeneous interactions are still allowed. Using variational inference, we derive an inference model capable of accurately learn node types, communities and node ranking.

Copyright (c) 2021 Laura Iacovissi and Caterina De Bacco.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

What's included

• src : Contains the Python implementation of XOR algorithm, the code for performing the cross-validation procedure and the code for generating benchmark synthetic data with intrinsic community and hierarchical structure. More information can be found here.
• data/input : Contains an example of directed network having an intrinsic community structure and a given reciprocity value, and some example files to initialize the latent variables. They are synthetic data.
• data/output : Contains some results to test the code.

How to use

To download this repository, copy and paste the following:

git clone https://github.com/liacov/XOR-rankcom

The project has been developed using Python 3.7 with the packages contained in requirements.txt. We suggest to create a conda environment with conda create --name XOR python=3.7.9 --no-default-packages, activate it with conda activate XOR, and install all the dependencies by running (inside XOR directory):

pip install -r requirements.txt

For more information, refer to the documentation provided here.

Want to try it?

Check out the notebook demo src/XOR_demo.ipynb.