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：多年来,图表采矿算法在众多领域一直发挥着重要的作用,然而,尽管这些算法在各种图表分析任务方面表现良好,但大多数这些算法都缺乏公平考虑,因此,在以人为中心的应用中,这些算法可能导致对某些人群的歧视;最近,在以图表为基础的应用中,对算法公平进行了广泛研究;与独立和同样分布的数据(即d.)的算法公平相反,图采矿的公平性具有独家背景、分类和满足技术;在本次调查中,我们在公平图解开采的背景下,对现有文献进行了全面的最新介绍。具体地说,我们提出了图表公平概念的新分类,揭示了这些图的关联和差异。我们进一步介绍了促进图采矿公平的现有技术的有组织汇总。最后,我们总结了这一新兴研究领域广泛使用的数据集,并提供了对当前研究挑战和开放问题的深入了解,目的是鼓励交叉生成的构想和进一步的进展。