GCondenser: Benchmarking Graph Condensation

Large-scale graphs are valuable for graph representation learning, but the vast volume of data often hinders model building efficiency. Graph condensation (GC) addresses this challenge by compressing a large graph into a significantly smaller one that still supports effective model training. While recent studies have proposed various techniques to enhance condensation effectiveness, comprehensive and practical evaluations of these methods remain limited. In this paper, we introduce GCondenser, a large-scale graph condensation toolkit designed to facilitate flexible development, holistic evaluation and comparison of mainstream GC approaches. GCondenser provides a standardised GC pipeline with condensation, validation, and evaluation stages, and offers straightforward extensibility to accommodate new methods and datasets. Additionally, we conduct a thorough empirical study of existing GC methods, offering insights into multiple facets of condensation performance. The toolkit is available at https://github.com/superallen13/GCondenser.