This survey provides an overview of graph condensation (GC), a technique that synthesizes a compact yet informative graph to enable graph neural networks (GNNs) to achieve performance comparable to those trained on the original graph. GC addresses challenges in storage, transmission, and training of GNNs on large-scale graph data. The paper categorizes existing GC methods into five groups based on critical evaluation criteria: effectiveness, generalization, efficiency, fairness, and robustness. It examines various methods under each category and discusses two essential components: optimization strategies and condensed graph generation. The paper also empirically compares and analyzes representative GC methods based on these criteria and explores applications, open-source libraries, and challenges in GC. The survey aims to promote future research by providing a structured taxonomy and comprehensive insights into GC methods. The paper is organized into sections covering the definition, criteria, taxonomy, optimization strategies, and methods of GC. It highlights the importance of GC in various applications and discusses its potential for future development. The survey emphasizes the need for systematic research to advance GC methods and their applications.This survey provides an overview of graph condensation (GC), a technique that synthesizes a compact yet informative graph to enable graph neural networks (GNNs) to achieve performance comparable to those trained on the original graph. GC addresses challenges in storage, transmission, and training of GNNs on large-scale graph data. The paper categorizes existing GC methods into five groups based on critical evaluation criteria: effectiveness, generalization, efficiency, fairness, and robustness. It examines various methods under each category and discusses two essential components: optimization strategies and condensed graph generation. The paper also empirically compares and analyzes representative GC methods based on these criteria and explores applications, open-source libraries, and challenges in GC. The survey aims to promote future research by providing a structured taxonomy and comprehensive insights into GC methods. The paper is organized into sections covering the definition, criteria, taxonomy, optimization strategies, and methods of GC. It highlights the importance of GC in various applications and discusses its potential for future development. The survey emphasizes the need for systematic research to advance GC methods and their applications.