  Graph augmentation with contrastive learning has gained significant attention
in the field of recommendation systems due to its ability to learn expressive
user representations, even when labeled data is limited. However, directly
applying existing GCL models to real-world recommendation environments poses
challenges. There are two primary issues to address. Firstly, the lack of
consideration for data noise in contrastive learning can result in noisy
self-supervised signals, leading to degraded performance. Secondly, many
existing GCL approaches rely on graph neural network (GNN) architectures, which
can suffer from over-smoothing problems due to non-adaptive message passing. To
address these challenges, we propose a principled framework called GraphAug.
This framework introduces a robust data augmentor that generates denoised
self-supervised signals, enhancing recommender systems. The GraphAug framework
incorporates a graph information bottleneck (GIB)-regularized augmentation
paradigm, which automatically distills informative self-supervision information
and adaptively adjusts contrastive view generation. Through rigorous
experimentation on real-world datasets, we thoroughly assessed the performance
of our novel GraphAug model. The outcomes consistently unveil its superiority
over existing baseline methods. The source code for our model is publicly
available at: https://github.com/HKUDS/GraphAug.
