  Graph Neural Networks (GNNs) have attracted tremendous attention by
demonstrating their capability to handle graph data. However, they are
difficult to be deployed in resource-limited devices due to model sizes and
scalability constraints imposed by the multi-hop data dependency. In addition,
real-world graphs usually possess complex structural information and features.
Therefore, to improve the applicability of GNNs and fully encode the
complicated topological information, knowledge distillation on graphs (KDG) has
been introduced to build a smaller yet effective model and exploit more
knowledge from data, leading to model compression and performance improvement.
Recently, KDG has achieved considerable progress with many studies proposed. In
this survey, we systematically review these works. Specifically, we first
introduce KDG challenges and bases, then categorize and summarize existing
works of KDG by answering the following three questions: 1) what to distillate,
2) who to whom, and 3) how to distillate. Finally, we share our thoughts on
future research directions.
