Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering

• URL: http://arxiv.org/abs/2402.10756v1
• Date: Fri, 16 Feb 2024 15:25:56 GMT
• Title: Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
• Authors: Siamak Ghodsi, Seyed Amjad Seyedi, and Eirini Ntoutsi
• Abstract summary: iFairNMTF is an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization. Our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy.
• Score: 5.255750357176021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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