  Graph classification is a highly impactful task that plays a crucial role in
a myriad of real-world applications such as molecular property prediction and
protein function prediction.Aiming to handle the new classes with limited
labeled graphs, few-shot graph classification has become a bridge of existing
graph classification solutions and practical usage.This work explores the
potential of metric-based meta-learning for solving few-shot graph
classification.We highlight the importance of considering structural
characteristics in the solution and propose a novel framework which explicitly
considers global structure and local structure of the input graph. An
implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and
TRIANGLES, where extensive experiments validate the effectiveness of the
proposed method. The Chembl is constructed to fill in the gap of lacking
large-scale benchmark for few-shot graph classification evaluation, which is
released together with the implementation of SMF-GIN at:
https://github.com/jiangshunyu/SMF-GIN.
