# Reducing Access Disparities in Networks using Edge Augmentation

arXiv: https://arxiv.org/abs/2209.07616 · HTML: https://mlsanity.com/paper/2209.07616/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer](https://mlsanity.com/paper/2308.09499/) (2023)
- [Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction](https://mlsanity.com/paper/2309.17417/) (2023)
- [Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance](https://mlsanity.com/paper/2305.03223/) (2023)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)

## Abstract (arXiv)

In social networks, a node's position is a form of itsocial capital. Better-positioned members not only benefit from (faster) access to diverse information, but innately have more potential influence on information spread. Structural biases often arise from network formation, and can lead to significant disparities in information access based on position. Further, processes such as link recommendation can exacerbate this inequality by relying on network structure to augment connectivity. We argue that one can understand and quantify this social capital through the lens of information flow in the network. We consider the setting where all nodes may be sources of distinct information, and a node's (dis)advantage deems its ability to access all information available on the network. We introduce three new measures of advantage (broadcast, influence, and control), which are quantified in terms of position in the network using itaccess signatures -- vectors that represent a node's ability to share information. We then consider the problem of improving equity by making interventions to increase the access of the least-advantaged nodes. We argue that edge augmentation is most appropriate for mitigating bias in the network structure, and frame a budgeted intervention problem for maximizing minimum pairwise access. Finally, we propose heuristic strategies for selecting edge augmentations and empirically evaluate their performance on a corpus of real-world social networks. We demonstrate that a small number of interventions significantly increase the broadcast measure of access for the least-advantaged nodes (over 5 times more than random), and also improve the minimum influence. Additional analysis shows that these interventions can also dramatically shrink the gap in advantage between nodes (over %82) and reduce disparities between their access signatures.