S-Mixup: Structural Mixup for Graph Neural Networks
Junghurn Kim Sukwon Yun Chanyoung Park
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}