  Graph neural networks (GNNs) enable the analysis of graphs using deep
learning, with promising results in capturing structured information in graphs.
This paper focuses on creating a small graph to represent the original graph,
so that GNNs trained on the size-reduced graph can make accurate predictions.
We view the original graph as a distribution of receptive fields and aim to
synthesize a small graph whose receptive fields share a similar distribution.
Thus, we propose Graph Condesation via Receptive Field Distribution Matching
(GCDM), which is accomplished by optimizing the synthetic graph through the use
of a distribution matching loss quantified by maximum mean discrepancy (MMD).
Additionally, we demonstrate that the synthetic graph generated by GCDM is
highly generalizable to a variety of models in evaluation phase and that the
condensing speed is significantly improved using this framework.
