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直接学习隐藏图分布的卓越能力而备受关注。尽管这些技术取得了初步成功，但与许多现有深度生成方法类似，它们需要大量训练样本来学习优质模型。然而在罕见病药物发现等场景中，大规模训练样本往往难以获取。与此同时，少样本学习的最新进展为训练数据受限的应用打开了新大门。本文首次提出图生成式建模的少样本范式，为此我们开发了基于元学习的少样本标记图生成框架GSHOT。该框架学习从相似的辅助图数据集中迁移元知识，并通过自步调微调快速适应未见过的图数据集。通过在多个领域训练样本有限的数据集上进行大量实验，我们证明GSHOT生成的图相较于现有基线方法具有更优的保真度。