New FairRARI framework enhances fairness in PageRank algorithm

Researchers have introduced FairRARI, a novel framework designed to address fairness concerns within the PageRank algorithm. This plug-and-play system utilizes a convex optimization approach to compute PageRank vectors while adhering to group-fairness criteria based on sensitive vertex attributes. Experiments on real-world datasets demonstrate that FairRARI effectively achieves desired fairness levels and outperforms existing methods in terms of utility. AI

IMPACT Introduces a method to ensure fairness in graph-based algorithms, potentially impacting applications reliant on PageRank.

RANK_REASON The cluster describes a new academic paper introducing a novel algorithm and framework. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• DagsHub
• Emmanouil Kariotakis
• FairRARI
• Hugging Face
• IArxiv
• PageRank

• paper
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