# Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks

arXiv: https://arxiv.org/abs/2402.13787 · HTML: https://mlsanity.com/paper/2402.13787/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [Local Clustering on Complex Graphs and Complex Hypergraphs](https://mlsanity.com/paper/2412.03008/) (2024)
- [Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity](https://mlsanity.com/paper/2511.06568/) (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)
- [Towards Fair Graph Representation Learning in Social Networks](https://mlsanity.com/paper/2410.11493/) (2024)
- [Information Loss and Disparate Effects in Network Embeddings](https://mlsanity.com/paper/2509.12396/) (2025)

## Abstract (arXiv)

In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can reproduce and even amplify bias against minority groups in networks. Yet, their behavior differs: one one hand, we empirically show that PageRank mirrors the degree distribution for most of the ranking positions and it can equalize representation of minorities among the top ranked nodes; on the other hand, we find that HITS amplifies pre-existing bias in homophilic networks through a novel theoretical analysis, supported by empirical results. We find the root cause of bias amplification in HITS to be the level of homophily present in the network, modeled through an evolving network model with two communities. We illustrate our theoretical analysis on both synthetic and real datasets and we present directions for future work.