  Recent years, graph contrastive learning (GCL), which aims to learn
representations from unlabeled graphs, has made great progress. However, the
existing GCL methods mostly adopt human-designed graph augmentations, which are
sensitive to various graph datasets. In addition, the contrastive losses
originally developed in computer vision have been directly applied to graph
data, where the neighboring nodes are regarded as negatives and consequently
pushed far apart from the anchor. However, this is contradictory with the
homophily assumption of networks that connected nodes often belong to the same
class and should be close to each other. In this work, we propose an end-to-end
automatic GCL method, named NCLA to apply neighbor contrastive learning on
learnable graph augmentation. Several graph augmented views with adaptive
topology are automatically learned by the multi-head graph attention mechanism,
which can be compatible with various graph datasets without prior domain
knowledge. In addition, a neighbor contrastive loss is devised to allow
multiple positives per anchor by taking network topology as the supervised
signals. Both augmentations and embeddings are learned end-to-end in the
proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that
NCLA yields the state-of-the-art node classification performance on
self-supervised GCL and even exceeds the supervised ones, when the labels are
extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA.
