# Fairness and/or Privacy on Social Graphs

arXiv: https://arxiv.org/abs/2503.02114 · HTML: https://mlsanity.com/paper/2503.02114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Compositional Fairness Constraints for Graph Embeddings](https://mlsanity.com/paper/1905.10674/) (2019)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit](https://mlsanity.com/paper/1909.11793/) (2019)
- [Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?](https://mlsanity.com/paper/2306.00578/) (2023)
- [Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach](https://mlsanity.com/paper/2411.03663/) (2024)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness and privacy in GNNs, exploring the impact of various fairness-preserving measures on model performance. We conduct experiments across diverse datasets and evaluate the effectiveness of different fairness interventions. Our analysis considers the trade-offs between fairness, privacy, and accuracy, providing insights into the challenges and opportunities in achieving both fair and private graph learning. The results highlight the importance of carefully selecting and combining fairness-preserving measures based on the specific characteristics of the data and the desired fairness objectives. This study contributes to a deeper understanding of the complex interplay between fairness, privacy, and accuracy in GNNs, paving the way for the development of more robust and ethical graph learning models.