# Promoting Fairness in Information Access within Social Networks

arXiv: https://arxiv.org/abs/2512.14711 · HTML: https://mlsanity.com/paper/2512.14711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 73 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 23 of 23)

- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning](https://mlsanity.com/paper/2104.14210/) (2021)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [SNAP: A General Purpose Network Analysis and Graph Mining Library](https://mlsanity.com/paper/1606.07550/) (2016)
- [PageRank beyond the Web](https://mlsanity.com/paper/1407.5107/) (2014)
- [Group-Fairness in Influence Maximization](https://mlsanity.com/paper/1903.00967/) (2019)
- [Vital nodes identification in complex networks](https://mlsanity.com/paper/1607.01134/) (2016)
- [Graph Vulnerability and Robustness: A Survey](https://mlsanity.com/paper/2105.00419/) (2021)
- [Generalized Network Dismantling](https://mlsanity.com/paper/1801.01357/) (2018)
- [On the Fairness of Time-Critical Influence Maximization in Social Networks](https://mlsanity.com/paper/1905.06618/) (2019)
- [Gaps in Information Access in Social Networks](https://mlsanity.com/paper/1903.02047/) (2019)
- [Homophily and minority size explain perception biases in social networks](https://mlsanity.com/paper/1710.08601/) (2017)
- [Robust and Practical Solution of Laplacian Equations by Approximate Elimination](https://mlsanity.com/paper/2303.00709/) (2023)
- [Reducing Access Disparities in Networks using Edge Augmentation](https://mlsanity.com/paper/2209.07616/) (2022)
- [Maximizing Fair Content Spread via Edge Suggestion in Social Networks](https://mlsanity.com/paper/2207.07704/) (2022)
- [A Fast Algorithm for Moderating Critical Nodes via Edge Removal](https://mlsanity.com/paper/2309.06392/) (2023)
- [Kirchhoff Index As a Measure of Edge Centrality in Weighted Networks: Nearly Linear Time Algorithms](https://mlsanity.com/paper/1708.05959/) (2017)
- [Robust Vertex Enumeration for Convex Hulls in High Dimensions](https://mlsanity.com/paper/1802.01515/) (2018)
- [Current Flow Group Closeness Centrality for Complex Networks](https://mlsanity.com/paper/1802.02556/) (2018)
- [Improving information centrality of a node in complex networks by adding edges](https://mlsanity.com/paper/1804.06540/) (2018)

## Built on by (most cited first, 1 of 1)

- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)

## Abstract (arXiv)

The advent of online social networks has facilitated fast and wide spread of information. However, some users, especially members of minority groups, may be less likely to receive information spreading on the network, due to their disadvantaged network position. We study the optimization problem of adding new connections to a network to enhance fairness in information access among different demographic groups. We provide a concrete formulation of this problem where information access is measured in terms of resistance distance, {offering a new perspective that emphasizes global network structure and multi-path connectivity.} The problem is shown to be NP-hard. We propose a simple greedy algorithm which turns out to output accurate solutions, but its run time is cubic, which makes it undesirable for large networks. As our main technical contribution, we reduce its time complexity to linear, leveraging several novel approximation techniques. In addition to our theoretical findings, we also conduct an extensive set of experiments using both real-world and synthetic datasets. We demonstrate that our linear-time algorithm can produce accurate solutions for networks with millions of nodes.