Reducing Access Disparities in Networks using Edge Augmentation Article Swipe

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Ashkan Bashardoust , Sorelle A. Friedler , Carlos Scheidegger , Blair D. Sullivan , Suresh Venkatasubramanian · YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2209.07616

In social networks, a node's position is a form of \it{social 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 \it{access 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.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2209.07616

PDF

https://arxiv.org/pdf/2209.07616

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green

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10

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https://openalex.org/W4296415400

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OpenAlex ID

https://openalex.org/W4296415400

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DOI

https://doi.org/10.48550/arxiv.2209.07616

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Title

Reducing Access Disparities in Networks using Edge Augmentation

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Type

preprint

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Language

en

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Publication year

2022

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Publication date

2022-09-15

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Authors

Ashkan Bashardoust, Sorelle A. Friedler, Carlos Scheidegger, Blair D. Sullivan, Suresh Venkatasubramanian

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Landing page

https://arxiv.org/abs/2209.07616

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PDF URL

https://arxiv.org/pdf/2209.07616

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2209.07616

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Concepts

Pairwise comparison, Computer science, Node (physics), Enhanced Data Rates for GSM Evolution, Social network (sociolinguistics), Heuristic, Information access, Equity (law), Computer network, Distributed computing, Artificial intelligence, World Wide Web, Engineering, Social media, Political science, Structural engineering, Law

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0

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Related works (count)

10

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