Adversarial Graph Contrastive Learning with Information Regularization
This work addresses a key bottleneck in unsupervised graph learning for researchers and practitioners, though it is incremental as it builds on existing contrastive learning frameworks.
The paper tackles the challenge of generating high-quality contrastive samples for graph representation learning by proposing ARIEL, which introduces an adversarial graph view and information regularizer, achieving consistent improvements in node classification across real-world datasets and enhancing robustness.
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the models designed for images. Unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and much harder to provide high-quality contrastive samples, which are the key to the performance of contrastive learning models. This leaves much space for improvement over the existing graph contrastive learning frameworks. In this work, by introducing an adversarial graph view and an information regularizer, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within a reasonable constraint. It consistently outperforms the current graph contrastive learning methods in the node classification task over various real-world datasets and further improves the robustness of graph contrastive learning. The code is at https://github.com/Shengyu-Feng/ARIEL.