The burgeoning volume of graph data poses significant challenges in storage,
transmission, and particularly the training of graph neural networks (GNNs). To
address these challenges, graph condensation (GC) has emerged as an innovative
solution. GC focuses on synthesizing a compact yet highly representative graph,
on which GNNs can achieve performance comparable to trained on the large
original graph. The notable efficacy of GC and its broad prospects have
garnered significant attention and spurred extensive research. This survey
paper provides an up-to-date and systematic overview of GC, organizing existing
research into four categories aligned with critical GC evaluation criteria:
effectiveness, generalization, fairness, and efficiency. To facilitate an
in-depth and comprehensive understanding of GC, we examine various methods
under each category and thoroughly discuss two essential components within GC:
optimization strategies and condensed graph generation. Additionally, we
introduce the applications of GC in a variety of fields, and highlight the
present challenges and novel insights in GC, promoting advancements in future
research.

对图形凝聚进行了全面而深入的研究，提出了 GC 的四个关键评估标准，并详细讨论了优化策略和凝聚图生成这两个关键组成部分，同时介绍了 GC 在各领域的应用和未来研究中的挑战与观点。