  As training deep learning models on large dataset takes a lot of time and
resources, it is desired to construct a small synthetic dataset with which we
can train deep learning models sufficiently. There are recent works that have
explored solutions on condensing image datasets through complex bi-level
optimization. For instance, dataset condensation (DC) matches network gradients
w.r.t. large-real data and small-synthetic data, where the network weights are
optimized for multiple steps at each outer iteration. However, existing
approaches have their inherent limitations: (1) they are not directly
applicable to graphs where the data is discrete; and (2) the condensation
process is computationally expensive due to the involved nested optimization.
To bridge the gap, we investigate efficient dataset condensation tailored for
graph datasets where we model the discrete graph structure as a probabilistic
model. We further propose a one-step gradient matching scheme, which performs
gradient matching for only one single step without training the network
weights. Our theoretical analysis shows this strategy can generate synthetic
graphs that lead to lower classification loss on real graphs. Extensive
experiments on various graph datasets demonstrate the effectiveness and
efficiency of the proposed method. In particular, we are able to reduce the
dataset size by 90% while approximating up to 98% of the original performance
and our method is significantly faster than multi-step gradient matching (e.g.
15x in CIFAR10 for synthesizing 500 graphs). Code is available at
\url{https://github.com/amazon-research/DosCond}.
