individual Fair Nonnegative Matrix Tri-Factorization
This repository contains supplementary material on the iFairNMTF model. This model incorporates individual fairness within (unsupervised) graph clustering through a contrastive learning regularization all-in-all, represented as a Nonnegative Matrix Tri-Factorization (NMTF) framework. Our contribution is the first research work introducing fairness into NMF. This paper has been presented in the research track of PAKDD 2024 conference. In our paper entitled "Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering", we also investigate the implications of the real-world challenge of the clustering-fairness trade-off and provide a practical solution to this problem.
This repository consists of two directories: 1) Supplement: which includes the pdf supplement of our paper, and slides presented at the PAKDD 2024 conference. 2) main repository: including source codes, datasets, and evaluation metrics required to reproduce our results.
Please cite our paper if you use our material and/or source codes: [Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering](https://doi.org/10.1007/978-981-97-2242-6_23)
@inproceedings{DBLP:conf/pakdd/GhodsiSN24,
author = {Siamak Ghodsi and
Seyed Amjad Seyedi and
Eirini Ntoutsi},
editor = {De{-}Nian Yang and
Xing Xie and
Vincent S. Tseng and
Jian Pei and
Jen{-}Wei Huang and
Jerry Chun{-}Wei Lin},
title = {Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual
Fair Graph Clustering},
booktitle = {Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia
Conference on Knowledge Discovery and Data Mining, {PAKDD} 2024, Taipei,
Taiwan, May 7-10, 2024, Proceedings, Part {I}},
series = {Lecture Notes in Computer Science},
volume = {14645},
pages = {284--296},
publisher = {Springer},
year = {2024},
url = {https://doi.org/10.1007/978-981-97-2242-6\_23},
doi = {10.1007/978-981-97-2242-6\_23},
timestamp = {Tue, 07 May 2024 20:05:03 +0200},
biburl = {https://dblp.org/rec/conf/pakdd/GhodsiSN24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}