Fairness-aware PageRank via Edge Reweighting

TL;DR

This paper introduces a novel method for enhancing fairness in PageRank by reweighting existing edges, aiming to reduce bias while preserving the original network structure, and demonstrates its effectiveness through empirical evaluation.

Contribution

It proposes a new fairness-aware PageRank algorithm that reweights edges without altering network topology, using a projected gradient method to optimize fairness.

Findings

Small transition matrix modifications significantly improve fairness.

The method outperforms state-of-the-art baselines.

Fairness is achieved without adding or removing edges.

Abstract

Link-analysis algorithms, such as PageRank, are instrumental in understanding the structural dynamics of networks by evaluating the importance of individual vertices based on their connectivity. Recently, with the rising importance of responsible AI, the question of fairness in link-analysis algorithms has gained traction. In this paper, we present a new approach for incorporating group fairness into the PageRank algorithm by reweighting the transition probabilities in the underlying transition matrix. We formulate the problem of achieving fair PageRank by seeking to minimize the fairness loss, which is the difference between the original group-wise PageRank distribution and a target PageRank distribution. We further define a group-adapted fairness notion, which accounts for group homophily by considering random walks with group-biased restart for each group. Since the fairness loss is non-convex, we propose an efficient projected gradient-descent method for computing locally-optimal edge weights. Unlike earlier approaches, we do not recommend adding new edges to the network, nor do we adjust the restart vector. Instead, we keep the topology of the underlying network unchanged and only modify the relative importance of existing edges. We empirically compare our approach with state-of-the-art baselines and demonstrate the efficacy of our method, where very small changes in the transition matrix lead to significant improvement in the fairness of the PageRank algorithm.