Graph condensation (GC) is an emerging technique designed to learn a
significantly smaller graph that retains the essential information of the
original graph. This condensed graph has shown promise in accelerating graph
neural networks while preserving performance comparable to those achieved with
the original, larger graphs. Additionally, this technique facilitates
downstream applications such as neural architecture search and enhances our
understanding of redundancy in large graphs. Despite the rapid development of
GC methods, a systematic evaluation framework remains absent, which is
necessary to clarify the critical designs for particular evaluative aspects.
Furthermore, several meaningful questions have not been investigated, such as
whether GC inherently preserves certain graph properties and offers robustness
even without targeted design efforts. In this paper, we introduce GC-Bench, a
comprehensive framework to evaluate recent GC methods across multiple
dimensions and to generate new insights. Our experimental findings provide a
deeper insights into the GC process and the characteristics of condensed
graphs, guiding future efforts in enhancing performance and exploring new
applications. Our code is available at
https://github.com/Emory-Melody/GraphSlim/tree/main/benchmark.

图压缩是一种新兴技术，通过学习一个显著较小但保留原图关键信息的压缩图，加速图神经网络并保持性能，在神经架构搜索等下游应用和对大规模图冗余性的理解方面具有潜力。本文介绍 GC-Bench，一个综合评估图压缩方法的框架，提供对 GC 过程和压缩图特性的深入洞察，指导未来的性能增强和探索新应用。