Given the prevalence of large-scale graphs in real-world applications, the
storage and time for training neural models have raised increasing concerns. To
alleviate the concerns, we propose and study the problem of graph condensation
for graph neural networks (GNNs). Specifically, we aim to condense the large,
original graph into a small, synthetic and highly-informative graph, such that
GNNs trained on the small graph and large graph have comparable performance. We
approach the condensation problem by imitating the GNN training trajectory on
the original graph through the optimization of a gradient matching loss and
design a strategy to condense node futures and structural information
simultaneously. Extensive experiments have demonstrated the effectiveness of
the proposed framework in condensing different graph datasets into informative
smaller graphs. In particular, we are able to approximate the original test
accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer, while
reducing their graph size by more than 99.9%, and the condensed graphs can be
used to train various GNN architectures.Comment: 16 pages, 4 figure