Fairness of Information Flow in Social Networks
| Autor: | Zeinab S. Jalali, Qilan Chen, Shwetha M. Srikanta, Weixiang Wang, Myunghwan Kim, Hema Raghavan, Sucheta Soundarajan |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | ACM Transactions on Knowledge Discovery from Data. 17:1-26 |
| ISSN: | 1556-472X 1556-4681 |
| DOI: | 10.1145/3578268 |
| Popis: | Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF , an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved. |
| Databáze: | OpenAIRE |
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