sd-fair-mod
Supplemental material for paper "Fair-mod: Fair Modular Community Detection"
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Supplemental material for paper "Fair-mod: Fair Modular Community Detection"
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README.md
Supplemental material for paper: "Fair-mod: Fair Modular Community Detection"
Supplemental material for paper "Fair-mod: Fair Modular Community Detection", published in the proceedings for Complex Networks and their Applications 2024. Publication available here: https://link.springer.com/chapter/10.1007/978-3-031-82435-7_8. See also pre-pub in the main folder of this repository.
This repository contains the implementation of the Fair-mod modularity-based community detection algorithm, with a weighted balance-based fairness. The implementation is based on the source code for Louvain community detection found in the NetworkX library, see source code here: (https://networkx.org/documentation/stable/_modules/networkx/algorithms/community/louvain.html).
If you use Fair-mod to support your research, consider citing:
Panayiotou, G., Magnani, M. (2025). Fair-mod: Fair Modular Community Detection.
In: Cherifi, H., Donduran, M., Rocha, L.M., Cherifi, C., Varol, O. (eds) Complex Networks & Their Applications XIII.
COMPLEX NETWORKS 2024. Studies in Computational Intelligence, vol 1189. Springer, Cham.
https://doi.org/10.1007/978-3-031-82435-7_8
Usage
The algorithm expects as input a NetworkX graph object. The graph should be undirected (directed graphs are not currently supported), and the sensitive attribute S for the graph should be coded as a node attribute named color, taking either of two values: red or blue. Future versions will address the limitations of the implementation.
The repository also includes:
* process_raw.py: Code to process the raw social network datasets featured in the paper, generating the desired NX objects.
* s_fair_sc.py: Code for the Scalable Fair Spectral Clustering (sFairSC) algorithm, translated from the original MATLAB version of the code in https://github.com/jiiwang/scalablefairspectral_clustering. Credit for the algorithm goes to the original authors:
[1] Ji Wang et al. (2023). Scalable Spectral Clustering with Group Fairness Constraints. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.
Owner
- Name: Georgios Panayiotou
- Login: giorgospanay
- Kind: user
- Company: Uppsala University
- Repositories: 1
- Profile: https://github.com/giorgospanay
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: 'Fair-mod: Fair Modular Community Detection'
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Georgios
family-names: Panayiotou
email: georgios.panayiotou@it.uu.se
orcid: 'https://orcid.org/0009-0002-3907-3189'
- given-names: Matteo
family-names: Magnani
email: matteo.magnani@it.uu.se
orcid: 'https://orcid.org/0000-0002-3437-9018'
identifiers:
- type: doi
value: 10.1007/978-3-031-82435-7_8
- type: url
value: >-
https://link.springer.com/chapter/10.1007/978-3-031-82435-7_8
license: BSD-3-Clause-Attribution
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