  Graph condensation (GC) has recently garnered considerable attention due to
its ability to reduce large-scale graph datasets while preserving their
essential properties. The core concept of GC is to create a smaller, more
manageable graph that retains the characteristics of the original graph.
Despite the proliferation of graph condensation methods developed in recent
years, there is no comprehensive evaluation and in-depth analysis, which
creates a great obstacle to understanding the progress in this field. To fill
this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to
analyze the performance of graph condensation in different scenarios
systematically. Specifically, GC-Bench systematically investigates the
characteristics of graph condensation in terms of the following dimensions:
effectiveness, transferability, and complexity. We comprehensively evaluate 12
state-of-the-art graph condensation algorithms in node-level and graph-level
tasks and analyze their performance in 12 diverse graph datasets. Further, we
have developed an easy-to-use library for training and evaluating different GC
methods to facilitate reproducible research. The GC-Bench library is available
at https://github.com/RingBDStack/GC-Bench.
