Towards Cohesion-Fairness Harmony: Contrastive Regularization in
  Individual Fair Graph Clustering
Siamak Ghodsi Seyed Amjad Seyedi Eirini Ntoutsi
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.