  The rapid development of Internet technology has given rise to a vast amount
of graph-structured data. Graph Neural Networks (GNNs), as an effective method
for various graph mining tasks, incurs substantial computational resource costs
when dealing with large-scale graph data. A data-centric manner solution is
proposed to condense the large graph dataset into a smaller one without
sacrificing the predictive performance of GNNs. However, existing efforts
condense graph-structured data through a computational intensive bi-level
optimization architecture also suffer from massive computation costs. In this
paper, we propose reforming the graph condensation problem as a Kernel Ridge
Regression (KRR) task instead of iteratively training GNNs in the inner loop of
bi-level optimization. More specifically, We propose a novel dataset
condensation framework (GC-SNTK) for graph-structured data, where a
Structure-based Neural Tangent Kernel (SNTK) is developed to capture the
topology of graph and serves as the kernel function in KRR paradigm.
Comprehensive experiments demonstrate the effectiveness of our proposed model
in accelerating graph condensation while maintaining high prediction
performance. The source code is available on
https://github.com/WANGLin0126/GCSNTK.
