Summary of A Semidefinite Relaxation Approach For Fair Graph Clustering, by Sina Baharlouei et al.
A Semidefinite Relaxation Approach for Fair Graph Clustering
by Sina Baharlouei, Sadra Sabouri
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Social and Information Networks (cs.SI); Machine Learning (stat.ML)
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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 The paper introduces fair graph clustering techniques within the framework of the disparate impact doctrine, aiming to ensure equitable representation and treatment of diverse communities in network analysis. The authors propose a joint optimization problem that integrates clustering quality and fairness constraints. To solve this NP-hard problem, they employ a semidefinite relaxation approach for up to medium-sized graphs and develop a novel algorithm based on the alternative direction method of multipliers for larger graphs. Their formulation allows for tuning the trade-off between clustering quality and fairness. The paper presents experimental results on graphs generated from the standard stochastic block model, demonstrating the superiority of their approach in achieving an optimal accuracy-fairness trade-off compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure that when we group people or communities together based on how they’re connected, everyone gets a fair shot. Right now, some methods for doing this can actually make things worse by leaving certain groups out or giving them less attention. The authors came up with a new way to do this clustering that takes into account both how well the groups are defined and whether it’s fair to everyone involved. They used special math tricks to solve the problem, which worked really well on some test cases. |
Keywords
» Artificial intelligence » Attention » Clustering » Optimization
