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This content will become publicly available on April 25, 2023

Title: Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based con- trastive 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.
Authors:
; ; ;
Award ID(s):
1947135
Publication Date:
NSF-PAR ID:
10332507
Journal Name:
Adversarial Graph Contrastive Learning with Information Regularization
Page Range or eLocation-ID:
1362 to 1371
Sponsoring Org:
National Science Foundation
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