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}

翻译：现有将混合增强技术应用于图数据的研究主要聚焦于图分类任务，而针对节点分类的研究仍显不足。本文提出一种新型的节点分类混合增强方法——结构化混合增强（S-Mixup），其核心思想是在混合节点时融入结构信息。具体而言，S-Mixup通过图神经网络（GNN）分类器为图中未标注节点生成伪标签及其预测置信度，这些信息作为构建类内与类间混合池的准则。此外，我们利用GNN训练过程中获得的边梯度，提出基于梯度的边选择策略，为混合生成的节点选取待连接的边。通过在真实基准数据集上的大量实验，我们验证了S-Mixup在节点分类任务上的有效性。实验表明，S-Mixup能够增强GNN的鲁棒性与泛化能力，尤其在同质性较低的场景中效果显著。S-Mixup的源代码可通过\url{https://github.com/SukwonYun/S-Mixup}获取。