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

arXiv: https://arxiv.org/abs/2402.10756 · HTML: https://mlsanity.com/paper/2402.10756/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Operationalizing Individual Fairness with Pairwise Fair Representations](https://mlsanity.com/paper/1907.01439/) (2019)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Scalable Spectral Clustering with Group Fairness Constraints](https://mlsanity.com/paper/2210.16435/) (2022)
- [Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview](https://mlsanity.com/paper/2302.05995/) (2023)
- [Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions](https://mlsanity.com/paper/2105.03714/) (2021)
- [Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy](https://mlsanity.com/paper/2306.01699/) (2023)

## Built on by (most cited first, 4 of 4)

- [GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks](https://mlsanity.com/paper/2402.12937/) (2024)
- [Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb](https://mlsanity.com/paper/2407.16892/) (2024)
- [A Semidefinite Relaxation Approach for Fair Graph Clustering](https://mlsanity.com/paper/2410.15233/) (2024)
- [A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective](https://mlsanity.com/paper/2510.23507/) (2025)

## Abstract (arXiv)

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.