  Graph representation learning has long been an important yet challenging task
for various real-world applications. However, their downstream tasks are mainly
performed in the settings of supervised or semi-supervised learning. Inspired
by recent advances in unsupervised contrastive learning, this paper is thus
motivated to investigate how the node-wise contrastive learning could be
performed. Particularly, we respectively resolve the class collision issue and
the imbalanced negative data distribution issue. Extensive experiments are
performed on three real-world datasets and the proposed approach achieves the
SOTA model performance.
