Fairness-Aware PageRank

TL;DR

This paper addresses fairness in network algorithms by proposing modifications to PageRank that promote equitable treatment of nodes, balancing fairness with utility preservation through two novel approaches.

Contribution

It introduces formal fairness definitions for PageRank and proposes two new algorithms to achieve fairness while minimizing utility loss.

Findings

The proposed algorithms improve fairness in PageRank on real and synthetic graphs.

Experimental results demonstrate the effectiveness of the approaches in balancing fairness and utility.

The work highlights the importance of fairness considerations in network analysis algorithms.

Abstract

Algorithmic fairness has attracted significant attention in the past years. Surprisingly, there is little work on fairness in networks. In this work, we consider fairness for link analysis algorithms and in particular for the celebrated PageRank algorithm. We provide definitions for fairness, and propose two approaches for achieving fairness. The first modifies the jump vector of the Pagerank algorithm to enfonce fairness, and the second imposes a fair behavior per node. We also consider the problem of achieving fairness while minimizing the utility loss with respect to the original algorithm. We present experiments with real and synthetic graphs that examine the fairness of Pagerank and demonstrate qualitatively and quantitatively the properties of our algorithms.