  Graph neural networks~(GNNs) apply deep learning techniques to
graph-structured data and have achieved promising performance in graph
representation learning. However, existing GNNs rely heavily on enough labels
or well-designed negative samples. To address these issues, we propose a new
self-supervised graph representation method: deep graph bootstrapping~(DGB).
DGB consists of two neural networks: online and target networks, and the input
of them are different augmented views of the initial graph. The online network
is trained to predict the target network while the target network is updated
with a slow-moving average of the online network, which means the online and
target networks can learn from each other. As a result, the proposed DGB can
learn graph representation without negative examples in an unsupervised manner.
In addition, we summarize three kinds of augmentation methods for
graph-structured data and apply them to the DGB. Experiments on the benchmark
datasets show the DGB performs better than the current state-of-the-art methods
and how the augmentation methods affect the performances.
