  Graph classification aims to extract accurate information from
graph-structured data for classification and is becoming more and more
important in graph learning community. Although Graph Neural Networks (GNNs)
have been successfully applied to graph classification tasks, most of them
overlook the scarcity of labeled graph data in many applications. For example,
in bioinformatics, obtaining protein graph labels usually needs laborious
experiments. Recently, few-shot learning has been explored to alleviate this
problem with only given a few labeled graph samples of test classes. The shared
sub-structures between training classes and test classes are essential in
few-shot graph classification. Exiting methods assume that the test classes
belong to the same set of super-classes clustered from training classes.
However, according to our observations, the label spaces of training classes
and test classes usually do not overlap in real-world scenario. As a result,
the existing methods don't well capture the local structures of unseen test
classes. To overcome the limitation, in this paper, we propose a direct method
to capture the sub-structures with well initialized meta-learner within a few
adaptation steps. More specifically, (1) we propose a novel framework
consisting of a graph meta-learner, which uses GNNs based modules for fast
adaptation on graph data, and a step controller for the robustness and
generalization of meta-learner; (2) we provide quantitative analysis for the
framework and give a graph-dependent upper bound of the generalization error
based on our framework; (3) the extensive experiments on real-world datasets
demonstrate that our framework gets state-of-the-art results on several
few-shot graph classification tasks compared to baselines.
