论文标题
图形分类的自动数据增强
Automated Data Augmentations for Graph Classification
论文作者
论文摘要
数据增强可有效改善学习机的不变性。我们认为，数据增强的核心挑战在于设计保留标签的数据转换。对于图像而言，这相对简单，但对于图形来说更具挑战性。在这项工作中，我们提出了GraphAug，这是一种新型的自动数据增强方法，旨在计算图形分类的标签不变增强。 GraphAug不像在现有研究中那样使用统一的转换，而是使用自动化的增强模型来避免损害图形的关键标签相关信息，从而在大多数时候产生标签不变的增强。为了确保标签不变性，我们开发了一种基于强化学习的培训方法，以最大程度地提高估计的标签不变性概率。实验表明，在各种图形分类任务上，Graphaug优于先前的图形增强方法。
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. Instead of using uniform transformations as in existing studies, GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Experiments show that GraphAug outperforms previous graph augmentation methods on various graph classification tasks.
