  Graph representation learning plays a vital role in processing
graph-structured data. However, prior arts on graph representation learning
heavily rely on labeling information. To overcome this problem, inspired by the
recent success of graph contrastive learning and Siamese networks in visual
representation learning, we propose a novel self-supervised approach in this
paper to learn node representations by enhancing Siamese self-distillation with
multi-scale contrastive learning. Specifically, we first generate two augmented
views from the input graph based on local and global perspectives. Then, we
employ two objectives called cross-view and cross-network contrastiveness to
maximize the agreement between node representations across different views and
networks. To demonstrate the effectiveness of our approach, we perform
empirical experiments on five real-world datasets. Our method not only achieves
new state-of-the-art results but also surpasses some semi-supervised
counterparts by large margins. Code is made available at
https://github.com/GRAND-Lab/MERIT
