Fairness-aware PageRank via Edge Reweighting
Honglian Wang, Haoyun Chen, Aristides Gionis

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Ethics and Social Impacts of AI
