# Homophily and minority size explain perception biases in social networks

arXiv: https://arxiv.org/abs/1710.08601 · HTML: https://mlsanity.com/paper/1710.08601/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

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

- [FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning](https://mlsanity.com/paper/2104.14210/) (2021)
- [FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification](https://mlsanity.com/paper/2201.11596/) (2022)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Promoting Fairness in Link Prediction with Graph Enhancement](https://mlsanity.com/paper/2409.08658/) (2024)
- [Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks](https://mlsanity.com/paper/2302.11479/) (2023)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Minorities in networks and algorithms](https://mlsanity.com/paper/2206.07113/) (2022)

## Abstract (arXiv)

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show analytically that these biases depend on the level of homophily and its asymmetric nature, as well as on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations on six real-world networks. We also show under what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.