  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.
