  Graph representation learning, a critical step in graph-centric tasks, has
seen significant advancements. Earlier techniques often operate in an
end-to-end setting, where performance heavily relies on the availability of
ample labeled data. This constraint has spurred the emergence of few-shot
learning on graphs, where only a few task-specific labels are available for
each task. Given the extensive literature in this field, this survey endeavors
to synthesize recent developments, provide comparative insights, and identify
future directions. We systematically categorize existing studies into three
major families: meta-learning approaches, pre-training approaches, and hybrid
approaches, with a finer-grained classification in each family to aid readers
in their method selection process. Within each category, we analyze the
relationships among these methods and compare their strengths and limitations.
Finally, we outline prospective future directions for few-shot learning on
graphs to catalyze continued innovation in this field.
