Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
This work addresses individual fairness in graph clustering for users needing interpretable and cohesive clusters, representing an incremental improvement over existing methods.
The paper tackled the problem of balancing fairness and cohesion in graph clustering by proposing iFairNMTF, a model that uses contrastive fairness regularization to achieve customizable trade-offs, resulting in superior flexibility in fairness and clustering performance on real and synthetic datasets.
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.