  Graph representation learning nowadays becomes fundamental in analyzing
graph-structured data. Inspired by recent success of contrastive methods, in
this paper, we propose a novel framework for unsupervised graph representation
learning by leveraging a contrastive objective at the node level. Specifically,
we generate two graph views by corruption and learn node representations by
maximizing the agreement of node representations in these two views. To provide
diverse node contexts for the contrastive objective, we propose a hybrid scheme
for generating graph views on both structure and attribute levels. Besides, we
provide theoretical justification behind our motivation from two perspectives,
mutual information and the classical triplet loss. We perform empirical
experiments on both transductive and inductive learning tasks using a variety
of real-world datasets. Experimental experiments demonstrate that despite its
simplicity, our proposed method consistently outperforms existing
state-of-the-art methods by large margins. Moreover, our unsupervised method
even surpasses its supervised counterparts on transductive tasks, demonstrating
its great potential in real-world applications.
