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众多领域发挥着重要作用。然而，尽管这些算法在各种图分析任务中表现出色，但大多数算法缺乏对公平性的考量。因此，当它们被应用于以人为中心的情景时，可能会导致对某些群体的歧视。近年来，算法公平性在图基应用中得到了广泛研究。与独立同分布数据上的算法公平性不同，图挖掘中的公平性具有独特的背景、分类体系及实现技术。本综述全面且及时地介绍了公平图挖掘现有文献。具体而言，我们提出了一种新颖的图公平性概念分类法，揭示了不同概念之间的联系与区别。随后，我们系统总结了现有促进图挖掘公平性的技术。最后，我们归纳了这一新兴研究领域广泛使用的数据集，并针对当前研究挑战与开放性问题提出了见解，旨在促进跨领域思想碰撞与进一步进展。