# Inequality and Inequity in Network-based Ranking and Recommendation Algorithms

arXiv: https://arxiv.org/abs/2110.00072 · HTML: https://mlsanity.com/paper/2110.00072/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2021.

## Built on by (most cited first, 7 of 7)

- [Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://mlsanity.com/paper/2206.04500/) (2022)
- [Fairness-aware PageRank via Edge Reweighting](https://mlsanity.com/paper/2512.08055/) (2025)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)
- [FairRARI: A Plug and Play Framework for Fairness-Aware PageRank](https://mlsanity.com/paper/2602.08589/) (2026)
- [Minorities in networks and algorithms](https://mlsanity.com/paper/2206.07113/) (2022)
- [Delayed and Indirect Impacts of Link Recommendations](https://mlsanity.com/paper/2303.09700/) (2023)
- [Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study](https://mlsanity.com/paper/2608.09899/) (2026)

## Abstract (arXiv)

Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.