Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
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DOI：
10.1609/aaai.v34i01.5429
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Farzan Masrour;T. Wilson;Heng Yan;P. Tan;A. Esfahanian
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文献类型：
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作者：
Farzan Masrour;T. Wilson;Heng Yan;P. Tan;A. Esfahanian
链接预测是在线社交网络中的一项重要任务，因为它可以用来推断网络中新的或以前未知的关系。然而，由于同质性原则，目前的算法容易促进链接，这可能导致网络隔离的增加，这种效应被称为过滤气泡。在这项研究中，我们研究了过滤器气泡问题的角度来看，算法的公平性，并介绍了一个二元级的公平性标准的基础上，网络模块化措施。我们展示了如何利用该标准作为后处理步骤，以产生更多的异构链接，以克服过滤器气泡问题。此外，我们还提出了一个新的框架，将对抗网络表示学习与监督链接预测相结合，以减轻过滤器气泡问题。在几个真实数据集上进行的实验结果表明，与其他基线方法相比，所提出的方法是有效的，其中包括传统的链接预测和公平性感知的方法，用于i.i.d数据。
Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.