Fairness in Graph Mining: A Survey
Fairness in Graph Mining: A Survey
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DOI：
10.1109/tkde.2023.3265598
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发表时间：
2022-04
影响因子：
8.9
通讯作者：
Yushun Dong;Jing Ma;Song Wang;Chen Chen-Chen;Jundong Li
中科院分区：
文献类型：
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作者：
Yushun Dong;Jing Ma;Song Wang;Chen Chen-Chen;Jundong Li
多年来，图挖掘算法一直在无数领域发挥着重要作用。然而，尽管它们在各种图分析任务上表现良好，但大多数算法缺乏公平性考虑。因此，当它们在以人为本的应用程序中被利用时，可能会导致对某些人群的歧视。最近，算法公平性在基于图的应用中得到了广泛的研究。与独立同分布（i.i.d.）数据上的算法公平性相比，图挖掘中的公平性具有独特的背景、分类法和实现技术。在本次调查中，我们对公平图挖掘背景下的现有文献进行了全面且最新的介绍。具体来说，我们提出了一种新颖的图公平概念分类法，阐明了它们的联系和差异。我们进一步对促进图挖掘公平性的现有技术进行了有组织的总结。最后，我们讨论当前的研究挑战和悬而未决的问题，旨在鼓励杂交育种的想法和进一步的进展。
Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we discuss current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.