  We study graph data augmentation by mixup, which has been used successfully
on images. A key operation of mixup is to compute a convex combination of a
pair of inputs. This operation is straightforward for grid-like data, such as
images, but challenging for graph data. The key difficulty lies in the fact
that different graphs typically have different numbers of nodes, and thus there
lacks a node-level correspondence between graphs. In this work, we propose
S-Mixup, a simple yet effective mixup method for graph classification by soft
alignments. Specifically, given a pair of graphs, we explicitly obtain
node-level correspondence via computing a soft assignment matrix to match the
nodes between two graphs. Based on the soft assignments, we transform the
adjacency and node feature matrices of one graph, so that the transformed graph
is aligned with the other graph. In this way, any pair of graphs can be mixed
directly to generate an augmented graph. We conduct systematic experiments to
show that S-Mixup can improve the performance and generalization of graph
neural networks (GNNs) on various graph classification tasks. In addition, we
show that S-Mixup can increase the robustness of GNNs against noisy labels.
