Graph Contrastive Learning with Generative Adversarial Network
Cheng Wu Chaokun Wang Jingcao Xu Ziyang Liu Kai Zheng Xiaowei Wang Yang Song Kun Gai
Graph Neural Networks (GNNs) have demonstrated promising results on
exploiting node representations for many downstream tasks through supervised
end-to-end training. To deal with the widespread label scarcity issue in
real-world applications, Graph Contrastive Learning (GCL) is leveraged to train
GNNs with limited or even no labels by maximizing the mutual information
between nodes in its augmented views generated from the original graph.
However, the distribution of graphs remains unconsidered in view generation,
resulting in the ignorance of unseen edges in most existing literature, which
is empirically shown to be able to improve GCL's performance in our
experiments. To this end, we propose to incorporate graph generative
adversarial networks (GANs) to learn the distribution of views for GCL, in
order to i) automatically capture the characteristic of graphs for
augmentations, and ii) jointly train the graph GAN model and the GCL model.
Specifically, we present GACN, a novel Generative Adversarial Contrastive
learning Network for graph representation learning. GACN develops a view
generator and a view discriminator to generate augmented views automatically in
an adversarial style. Then, GACN leverages these views to train a GNN encoder
with two carefully designed self-supervised learning losses, including the
graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore,
we design an optimization framework to train all GACN modules jointly.
Extensive experiments on seven real-world datasets show that GACN is able to
generate high-quality augmented views for GCL and is superior to twelve
state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly
discovers that the generated views in data augmentation finally conform to the
well-known preferential attachment rule in online networks.