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.

综合分析了最近的 Graph 表示学习的发展情况，并提供了比较和未来方向，将现有研究系统分类为元学习方法、预训练方法和混合方法，并比较它们的优势和局限性，为 Few-shot 学习在图上的未来方向提供了引导。