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

arXiv:2402.10756v1 Announce Type: new
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 …

abstract arxiv challenges cluster clustering constraints cs.ai cs.it cs.lg cs.si face fair fairness graph math.it partitioning regularization type