Generative Few-shot Graph Classification: An Adaptive Perspective

The task of few-shot graph classification aims to assign class labels to graph samples, where only a limited number of labeled graphs are provided for each class. To deal with the problem brought about by label scarcity, recent works have focused on adopting the prevalent few-shot learning framework to ensure fast adaptations to classes with limited labeled graphs. In general, these studies propose to accumulate meta-knowledge across various base classes with sufficient labeled graphs, and then generalize such meta-knowledge to novel classes, which are disjoint from base classes and consist of limited labeled graphs. However, existing studies generally ignore the distinct distribution shifts between base classes and novel classes, leading to unsatisfactory adaptation performance. On the other hand, it remains challenging to address this issue due to the potential variance in distributions between classes. To tackle this problem, we propose a novel generative few-shot graph classification framework that can promote adaptation performance by generating adaptive structures for graphs in novel classes. Our framework incorporates a generative model to modify the graph structures for adaptation. We further conduct extensive experiments to validate the effectiveness of our framework.