Few-shot graph classification aims at predicting classes for graphs, given
limited labeled graphs for each class. To tackle the bottleneck of label
scarcity, recent works propose to incorporate few-shot learning frameworks for
fast adaptations to graph classes with limited labeled graphs. Specifically,
these works propose to accumulate meta-knowledge across diverse meta-training
tasks, and then generalize such meta-knowledge to the target task with a
disjoint label set. However, existing methods generally ignore task
correlations among meta-training tasks while treating them independently.
Nevertheless, such task correlations can advance the model generalization to
the target task for better classification performance. On the other hand, it
remains non-trivial to utilize task correlations due to the complex components
in a large number of meta-training tasks. To deal with this, we propose a novel
few-shot learning framework FAITH that captures task correlations via
constructing a hierarchical task graph at different granularities. Then we
further design a loss-based sampling strategy to select tasks with more
correlated classes. Moreover, a task-specific classifier is proposed to utilize
the learned task correlations for few-shot classification. Extensive
experiments on four prevalent few-shot graph classification datasets
demonstrate the superiority of FAITH over other state-of-the-art baselines.

提出了一种新的少样本学习框架 FAITH，通过构建分层任务图并设计基于损失的采样策略来捕获任务相关性，利用学习到的任务之间的相关性进行少样本图分类，实验结果表明其在四个流行的少样本图分类数据集上表现优于其他现有方法。