Summary of Towards Cohesion-fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering, by Siamak Ghodsi et al.
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
by Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel individual Fairness Nonnegative Matrix Tri-Factorization model called iFairNMTF is proposed to address two primary challenges in conventional fair graph clustering methods. These challenges include prioritizing balanced clusters at the expense of cluster cohesion by imposing rigid constraints, and a lack of interpretability in existing fairness methods. The iFairNMTF model achieves balanced and cohesive clusters while introducing fairness regularization that allows for customizable accuracy-fairness trade-offs, enhancing user autonomy without compromising interpretability. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary iFairNMTF is a new way to group things together on a graph while making sure everyone gets along. Usually, these methods try to make all groups equal, but that can make them not very good at grouping similar things together. The iFairNMTF method lets you choose how important it is to have fair groups versus having good groups. This makes it better than other methods because you can decide what’s most important. It also helps us understand why the groups are like they are, which is useful. |
Keywords
* Artificial intelligence * Clustering * Regularization
