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.

翻译：深度图生成建模近年来因其能够直接学习隐藏的底层图分布的卓越能力而获得广泛关注。尽管这些技术取得了初步成功，但如同许多现有深度生成方法一样，它们需要大量训练样本来学习良好模型。不幸的是，在诸如罕见病药物发现等场景中，大量训练样本可能并不总是可用。与此同时，少样本学习的最新进展已为训练数据有限的应用打开了大门。本文中，我们引入了此前尚未探索的少样本图生成建模范式。为此，我们开发了GSHOT，一个基于元学习的少样本标注图生成建模框架。GSHOT学习从相似辅助图数据集中迁移元知识。利用这些先验经验，GSHOT通过自步微调快速适应未见过的图数据集。通过在来自不同领域且训练样本有限的数据集上进行广泛实验，我们证实GSHOT生成的图相比现有基线方法具有更高的保真度。