  Existing studies for applying the mixup technique on graphs mainly focus on
graph classification tasks, while the research in node classification is still
under-explored. In this paper, we propose a novel mixup augmentation for node
classification called Structural Mixup (S-Mixup). The core idea is to take into
account the structural information while mixing nodes. Specifically, S-Mixup
obtains pseudo-labels for unlabeled nodes in a graph along with their
prediction confidence via a Graph Neural Network (GNN) classifier. These serve
as the criteria for the composition of the mixup pool for both inter and
intra-class mixups. Furthermore, we utilize the edge gradient obtained from the
GNN training and propose a gradient-based edge selection strategy for selecting
edges to be attached to the nodes generated by the mixup. Through extensive
experiments on real-world benchmark datasets, we demonstrate the effectiveness
of S-Mixup evaluated on the node classification task. We observe that S-Mixup
enhances the robustness and generalization performance of GNNs, especially in
heterophilous situations. The source code of S-Mixup can be found at
\url{https://github.com/SukwonYun/S-Mixup}
