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.

翻译：传统的公平图聚类方法面临两大挑战：i) 它们优先追求均衡聚类，通过施加刚性约束而牺牲了聚类凝聚力；ii) 现有的个体和群体层面公平性图划分方法大多依赖特征分解，因此通常缺乏可解释性。为解决这些问题，我们提出iFairNMTF，一种带有对比公平正则化的个体公平非负矩阵三因子分解模型，能够实现均衡且具有凝聚力的聚类。通过引入公平正则化，我们的模型允许可定制的准确性-公平性权衡，从而在不损害非负矩阵三因子分解带来的可解释性的前提下提升用户自主性。在真实和合成数据集上的实验评估表明，iFairNMTF在实现公平性和聚类性能方面具有卓越的灵活性。