FairRARI: A Plug and Play Framework for Fairness-Aware PageRank

TL;DR

FairRARI is a flexible convex optimization framework that computes fair PageRank vectors satisfying various group fairness criteria, ensuring fairness without sacrificing computational efficiency.

Contribution

It introduces a unified, in-processing convex optimization approach for fairness-aware PageRank, with guarantees on fairness levels and computational efficiency.

Findings

Outperforms existing methods in utility and fairness

Achieves fairness with the same asymptotic complexity as original PageRank

Effectively handles multiple group fairness criteria

Abstract

PageRank (PR) is a fundamental algorithm in graph machine learning tasks. Owing to the increasing importance of algorithmic fairness, we consider the problem of computing PR vectors subject to various group-fairness criteria based on sensitive attributes of the vertices. At present, principled algorithms for this problem are lacking - some cannot guarantee that a target fairness level is achieved, while others do not feature optimality guarantees. In order to overcome these shortcomings, we put forth a unified in-processing convex optimization framework, termed FairRARI, for tackling different group-fairness criteria in a ``plug and play'' fashion. Leveraging a variational formulation of PR, the framework computes fair PR vectors by solving a strongly convex optimization problem with fairness constraints, thereby ensuring that a target fairness level is achieved. We further introduce three different fairness criteria which can be efficiently tackled using FairRARI to compute fair PR vectors with the same asymptotic time-complexity as the original PR algorithm. Extensive experiments on real-world datasets showcase that FairRARI outperforms existing methods in terms of utility, while achieving the desired fairness levels across multiple vertex groups; thereby highlighting its effectiveness.