  Recently, contrastiveness-based augmentation surges a new climax in the
computer vision domain, where some operations, including rotation, crop, and
flip, combined with dedicated algorithms, dramatically increase the model
generalization and robustness. Following this trend, some pioneering attempts
employ the similar idea to graph data. Nevertheless, unlike images, it is much
more difficult to design reasonable augmentations without changing the nature
of graphs. Although exciting, the current graph contrastive learning does not
achieve as promising performance as visual contrastive learning. We conjecture
the current performance of graph contrastive learning might be limited by the
violation of the label-invariant augmentation assumption. In light of this, we
propose a label-invariant augmentation for graph-structured data to address
this challenge. Different from the node/edge modification and subgraph
extraction, we conduct the augmentation in the representation space and
generate the augmented samples in the most difficult direction while keeping
the label of augmented data the same as the original samples. In the
semi-supervised scenario, we demonstrate our proposed method outperforms the
classical graph neural network based methods and recent graph contrastive
learning on eight benchmark graph-structured data, followed by several in-depth
experiments to further explore the label-invariant augmentation in several
aspects.
