Automated Data Augmentations for Graph Classification

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-02
Main Authors: Luo, Youzhi, McThrow, Michael, Au, Wing Yee, Komikado, Tao, Uchino, Kanji, Maruhashi, Koji, Ji, Shuiwang
Format: Article
Language:English
Subjects:
Automation
Classification
Data augmentation
Invariance
Machine learning
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422