Fairness-Sensitive PageRank Approximation

2026-02-02

AI Summary

This work addresses the tendency of conventional PageRank to exacerbate influence disparities between groups in structurally unequal social networks, where fairness guarantees for minority groups are absent. The authors propose a fair PageRank estimation method grounded in mean-field approximation that integrates node in-degree, group labels, and global group proportions to derive a closed-form solution—eliminating the need for matrix inversion and substantially reducing computational complexity. Their approach enables, for the first time, an analysis of within-group variance of PageRank scores under explicit fairness constraints. Evaluated on real-world networks, the method achieves nearly an order-of-magnitude speedup while preserving accuracy and scalability, offering an efficient solution for large-scale fair ranking.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Computer Vision: Bias, Fairness & Privacy
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Real-world social networks have structural inequalities, including the majority and minorities, and fairness-agnostic centrality measures often amplify these inequalities by disproportionately favoring majority nodes. Fairness-Sensitive PageRank aims to balance algorithmic influence across structurally and demographically diverse groups while preserving the link-based relevance of classical PageRank. However, existing formulations require solving constrained matrix inversions that scale poorly with network size. In this work, we develop an efficient mean-field approximation for Fairness-Sensitive PageRank (FSPR) that enforces group-level fairness through an estimated teleportation (jump) vector, thereby avoiding the costly matrix inversion and iterative optimization. We derive a closed-form approximation of FSPR using the in-degree and group label of nodes, along with the global group proportion. We further analyze intra-class fluctuations by deriving expressions for the variance of approximated FSPR scores. Empirical results on real-world networks demonstrate that the proposed approximation efficiently estimates the FSPR while reducing runtime by an order of magnitude, enabling fairness-constrained ranking at scale.

Problem

• Fairness-Sensitive PageRank
• structural inequality
• centrality measures
• scalability
• social networks

Innovation

• Fairness-Sensitive PageRank
• mean-field approximation
• group-level fairness
• scalable ranking
• teleportation vector