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 summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.
翻译:图挖掘算法多年来在各个领域中扮演着重要的角色。然而,尽管这些算法在各种图分析任务中表现出色,但它们大多数缺乏公平性考虑。因此,当在以人为中心的应用中使用时,它们可能会导致对某些群体的歧视。最近,图形应用程序中的算法公平性得到了广泛研究。与独立和同分布(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、分类法和满足技术。在这篇综述中,我们提供了现有文献的全面最新介绍,其中包括公平图挖掘的上下文。具体而言,我们提出了图中公平性概念的新分类法,该分类法阐明了它们之间的联系和差异。我们进一步总结了推动图挖掘中公平性的现有技术的有组织摘要。最后,我们总结了这一新兴研究领域中广泛使用的数据集,并就当前的研究挑战和未解决问题提供了见解,旨在鼓励跨领域的想法交流和进一步发展。