  Deep graph generative modeling has gained enormous attraction in recent years
due to its impressive ability to directly learn the underlying hidden graph
distribution. Despite their initial success, these techniques, like much of the
existing deep generative methods, require a large number of training samples to
learn a good model. Unfortunately, large number of training samples may not
always be available in scenarios such as drug discovery for rare diseases. At
the same time, recent advances in few-shot learning have opened door to
applications where available training data is limited. In this work, we
introduce the hitherto unexplored paradigm of few-shot graph generative
modeling. Towards this, we develop GSHOT, a meta-learning based framework for
few-shot labeled graph generative modeling. GSHOT learns to transfer
meta-knowledge from similar auxiliary graph datasets. Utilizing these prior
experiences, GSHOT quickly adapts to an unseen graph dataset through self-paced
fine-tuning. Through extensive experiments on datasets from diverse domains
having limited training samples, we establish that GSHOT generates graphs of
superior fidelity compared to existing baselines.
