Large-scale graphs are valuable for graph representation learning, yet the
abundant data in these graphs hinders the efficiency of the training process.
Graph condensation (GC) alleviates this issue by compressing the large graph
into a significantly smaller one that still supports effective model training.
Although recent research has introduced various approaches to improve the
effectiveness of the condensed graph, comprehensive and practical evaluations
across different GC methods are neglected. This paper proposes the first
large-scale graph condensation benchmark, GCondenser, to holistically evaluate
and compare mainstream GC methods. GCondenser includes a standardised GC
paradigm, consisting of condensation, validation, and evaluation procedures, as
well as enabling extensions to new GC methods and datasets. With GCondenser, a
comprehensive performance study is conducted, presenting the effectiveness of
existing methods. GCondenser is open-sourced and available at
this https URL

大规模图的压缩是一个重要的研究领域，本文提出了第一个大规模图压缩基准，名为 GCondenser，为综合评估和比较主流的 GC 方法提供了标准化的框架和评估程序，并展示了现有方法的有效性。