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iFairNMTF

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.

Fig1

Cite us as

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}
}

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