  Mixup has shown considerable success in mitigating the challenges posed by
limited labeled data in image classification. By synthesizing samples through
the interpolation of features and labels, Mixup effectively addresses the issue
of data scarcity. However, it has rarely been explored in graph learning tasks
due to the irregularity and connectivity of graph data. Specifically, in node
classification tasks, Mixup presents a challenge in creating connections for
synthetic data. In this paper, we propose Geometric Mixup (GeoMix), a simple
and interpretable Mixup approach leveraging in-place graph editing. It
effectively utilizes geometry information to interpolate features and labels
with those from the nearby neighborhood, generating synthetic nodes and
establishing connections for them. We conduct theoretical analysis to elucidate
the rationale behind employing geometry information for node Mixup, emphasizing
the significance of locality enhancement-a critical aspect of our method's
design. Extensive experiments demonstrate that our lightweight Geometric Mixup
achieves state-of-the-art results on a wide variety of standard datasets with
limited labeled data. Furthermore, it significantly improves the generalization
capability of underlying GNNs across various challenging out-of-distribution
generalization tasks. Our code is available at
https://github.com/WtaoZhao/geomix.
