  We study the problem of few-shot graph classification across domains with
nonequivalent feature spaces by introducing three new cross-domain benchmarks
constructed from publicly available datasets. We also propose an
attention-based graph encoder that uses three congruent views of graphs, one
contextual and two topological views, to learn representations of task-specific
information for fast adaptation, and task-agnostic information for knowledge
transfer. We run exhaustive experiments to evaluate the performance of
contrastive and meta-learning strategies. We show that when coupled with
metric-based meta-learning frameworks, the proposed encoder achieves the best
average meta-test classification accuracy across all benchmarks. The source
code and data will be released here: https://github.com/kavehhassani/metagrl
