  Graph-structured datasets usually have irregular graph sizes and
connectivities, rendering the use of recent data augmentation techniques, such
as Mixup, difficult. To tackle this challenge, we present the first Mixup-like
graph augmentation method at the graph-level called Graph Transplant, which
mixes irregular graphs in data space. To be well defined on various scales of
the graph, our method identifies the sub-structure as a mix unit that can
preserve the local information. Since the mixup-based methods without special
consideration of the context are prone to generate noisy samples, our method
explicitly employs the node saliency information to select meaningful subgraphs
and adaptively determine the labels. We extensively validate our method with
diverse GNN architectures on multiple graph classification benchmark datasets
from a wide range of graph domains of different sizes. Experimental results
show the consistent superiority of our method over other basic data
augmentation baselines. We also demonstrate that Graph Transplant enhances the
performance in terms of robustness and model calibration.
