  Graph-structured data arise naturally in many different application domains.
By representing data as graphs, we can capture entities (i.e., nodes) as well
as their relationships (i.e., edges) with each other. Many useful insights can
be derived from graph-structured data as demonstrated by an ever-growing body
of work focused on graph mining. However, in the real-world, graphs can be both
large - with many complex patterns - and noisy which can pose a problem for
effective graph mining. An effective way to deal with this issue is to
incorporate "attention" into graph mining solutions. An attention mechanism
allows a method to focus on task-relevant parts of the graph, helping it to
make better decisions. In this work, we conduct a comprehensive and focused
survey of the literature on the emerging field of graph attention models. We
introduce three intuitive taxonomies to group existing work. These are based on
problem setting (type of input and output), the type of attention mechanism
used, and the task (e.g., graph classification, link prediction, etc.). We
motivate our taxonomies through detailed examples and use each to survey
competing approaches from a unique standpoint. Finally, we highlight several
challenges in the area and discuss promising directions for future work.
