  Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention
from both academia and industry because prevailing studies on heterogeneous
graphs often suffer from label sparsity. FLHG aims to tackle the performance
degradation in the face of limited annotated data and there have been numerous
recent studies proposing various methods and applications. In this paper, we
provide a comprehensive review of existing FLHG methods, covering challenges,
research progress, and future prospects. Specifically, we first formalize FLHG
and categorize its methods into three types: single-heterogeneity FLHG,
dual-heterogeneity FLHG, and multi-heterogeneity FLHG. Then, we analyze the
research progress within each category, highlighting the most recent and
representative developments. Finally, we identify and discuss promising
directions for future research in FLHG. To the best of our knowledge, this
paper is the first systematic and comprehensive review of FLHG.
