We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification. Observing that the omission of sub-structures does not necessarily change the class label of the whole graph, we develop the \textbf{GraphCrop} (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub-structure omission. In principle, GraphCrop utilizes a node-centric strategy to crop a contiguous subgraph from the original graph while maintaining its connectivity. By preserving the valid structure contexts for graph classification, we encourage GNNs to understand the content of graph structures in a global sense, rather than rely on a few key nodes or edges, which may not always be present. GraphCrop is parameter learning free and easy to implement within existing GNN-based graph classifiers. Qualitatively, GraphCrop expands the existing training set by generating novel and informative augmented graphs, which retain the original graph labels in most cases. Quantitatively, GraphCrop yields significant and consistent gains on multiple standard datasets, and thus enhances the popular GNNs to outperform the baseline methods.

本研究提出了一种新的方法，通过数据增强来模拟真实世界的误差，以提高图神经网络的泛化能力并将其推广到各种分类任务中。采用子图裁剪数据增强方法以保留有效的结构上下文，鼓励图神经网络更好地理解整体性的结构内容。该方法不需要参数学习且易于在现有图神经网络分类器中实施。实验结果表明该方法可有效提高模型分类准确率。

GraphCrop：用于图分类的子图裁剪