Fairness of Information Flow in Social Networks
Fairness of Information Flow in Social Networks
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DOI：
10.1145/3578268
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发表时间：
2022-12
影响因子：
3.6
通讯作者：
Zeinab S. Jalali;Qilan Chen;S. M. Srikanta;Weixiang Wang;Myunghwan Kim;Hema Raghavan;S. Soundarajan
中科院分区：
文献类型：
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作者：
Zeinab S. Jalali;Qilan Chen;S. M. Srikanta;Weixiang Wang;Myunghwan Kim;Hema Raghavan;S. Soundarajan
社交网络是人们生活的重要组成部分，个人往往根据通过这些网络传播的信息做出重要的人生决策。因此，了解不同受保护群体的个人是否能平等获取网络中的信息至关重要。在本文中，我们定义了信息不公平（IUF）度量标准，它量化了受保护群体之间在获取信息方面的不平等程度。然后，我们引入了MinIUF算法，该算法通过向网络添加边来减少信息流中的不平等。最后，我们针对不同类型网络中感兴趣的属性（如性别）对信息流进行了深入分析，以评估这些网络的结构是否允许群体平等获取网络中流动的信息。此外，我们还研究了此类网络中不公平的原因以及如何改善这种情况。
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.