  Graph neural networks (GNNs) have attracted widespread attention for their
impressive capability of graph representation learning. However, the increasing
prevalence of large-scale graphs presents a significant challenge for GNN
training due to their computational demands, limiting the applicability of GNNs
in various scenarios. In response to this challenge, graph condensation (GC) is
proposed as a promising acceleration solution, focusing on generating an
informative compact graph that enables efficient training of GNNs while
retaining performance. Despite the potential to accelerate GNN training,
existing GC methods overlook the quality of large training graphs during both
the training and inference stages. They indiscriminately emulate the training
graph distributions, making the condensed graphs susceptible to noises within
the training graph and significantly impeding the application of GC in
intricate real-world scenarios. To address this issue, we propose robust graph
condensation (RobGC), a plug-and-play approach for GC to extend the robustness
and applicability of condensed graphs in noisy graph structure environments.
Specifically, RobGC leverages the condensed graph as a feedback signal to guide
the denoising process on the original training graph. A label propagation-based
alternating optimization strategy is in place for the condensation and
denoising processes, contributing to the mutual purification of the condensed
graph and training graph. Additionally, as a GC method designed for inductive
graph inference, RobGC facilitates test-time graph denoising by leveraging the
noise-free condensed graph to calibrate the structure of the test graph.
Extensive experiments show that RobGC is compatible with various GC methods,
significantly boosting their robustness under different types and levels of
graph structural noises.
