# Minorities in networks and algorithms

arXiv: https://arxiv.org/abs/2206.07113 · HTML: https://mlsanity.com/paper/2206.07113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Modern temporal network theory: A colloquium](https://mlsanity.com/paper/1508.01303/) (2015)
- [Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks](https://mlsanity.com/paper/1307.4030/) (2013)
- [Predicting Scientific Success Based on Coauthorship Networks](https://mlsanity.com/paper/1402.7268/) (2014)
- [Reputation and Impact in Academic Careers](https://mlsanity.com/paper/1303.7274/) (2013)
- [Homophily and minority size explain perception biases in social networks](https://mlsanity.com/paper/1710.08601/) (2017)
- [Inequality and Inequity in Network-based Ranking and Recommendation Algorithms](https://mlsanity.com/paper/2110.00072/) (2021)
- [Link recommendations: Their impact on network structure and minorities](https://mlsanity.com/paper/2205.06048/) (2022)
- [Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists](https://mlsanity.com/paper/1704.05801/) (2017)
- [Mathematical model of gender bias and homophily in professional hierarchies](https://mlsanity.com/paper/1901.07600/) (2019)
- [Quantifying the impact of weak, strong, and super ties in scientific careers](https://mlsanity.com/paper/1509.01804/) (2015)
- [The Majority Illusion in Social Networks](https://mlsanity.com/paper/1506.03022/) (2015)
- [Mechanistic Models in Computational Social Science](https://mlsanity.com/paper/1507.00477/) (2015)
- [Sampling from Social Networks with Attributes](https://mlsanity.com/paper/1702.05427/) (2017)
- [Group mixing drives inequality in face-to-face gatherings](https://mlsanity.com/paper/2106.11688/) (2021)
- [Ranking nodes in growing networks: When PageRank fails](https://mlsanity.com/paper/1509.01476/) (2015)
- [Towards Quantifying Sampling Bias in Network Inference](https://mlsanity.com/paper/1803.02422/) (2018)
- [The Role of Network Structure and Initial Group Norm Distributions in Norm Conflict](https://mlsanity.com/paper/1907.00888/) (2019)

## Abstract (arXiv)

In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss dynamical processes on and of networks and the formation of norms and health inequalities. Additionally, we argue that network modeling is paramount for unveiling the effect of ranking and social recommendation algorithms on the visibility of minorities. Finally, we highlight the key challenges and future opportunities in this emerging research topic.