  Analytics on large-scale graphs have posed significant challenges to
computational efficiency and resource requirements. Recently, Graph
condensation (GC) has emerged as a solution to address challenges arising from
the escalating volume of graph data. The motivation of GC is to reduce the
scale of large graphs to smaller ones while preserving essential information
for downstream tasks. For a better understanding of GC and to distinguish it
from other related topics, we present a formal definition of GC and establish a
taxonomy that systematically categorizes existing methods into three types
based on its objective, and classify the formulations to generate the condensed
graphs into two categories as modifying the original graphs or synthetic
completely new ones. Moreover, our survey includes a comprehensive analysis of
datasets and evaluation metrics in this field. Finally, we conclude by
addressing challenges and limitations, outlining future directions, and
offering concise guidelines to inspire future research in this field.
