Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
<|reference_start|>Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering: Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these issues, we propose iFairNMTF, an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization that achieves balanced and cohesive clusters. By introducing fairness regularization, our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy without compromising the interpretability provided by nonnegative matrix tri-factorization. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance.<|reference_end|>
@article{ghodsi2024towards,
    title={Towards Cohesion-Fairness Harmony: Contrastive Regularization in
  Individual Fair Graph Clustering},
    author={Siamak Ghodsi, Seyed Amjad Seyedi, and Eirini Ntoutsi},
    journal={arXiv preprint arXiv:2402.10756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10756},
    primaryClass={cs.LG cs.AI cs.IT cs.SI math.IT}
}