  Graph contrastive learning has gained significant progress recently. However,
existing works have rarely explored non-aligned node-node contrasting. In this
paper, we propose a novel graph contrastive learning method named RoSA that
focuses on utilizing non-aligned augmented views for node-level representation
learning. First, we leverage the earth mover's distance to model the minimum
effort to transform the distribution of one view to the other as our
contrastive objective, which does not require alignment between views. Then we
introduce adversarial training as an auxiliary method to increase sampling
diversity and enhance the robustness of our model. Experimental results show
that RoSA outperforms a series of graph contrastive learning frameworks on
homophilous, non-homophilous and dynamic graphs, which validates the
effectiveness of our work. To the best of our awareness, RoSA is the first work
focuses on the non-aligned node-node graph contrastive learning problem. Our
codes are available at:
\href{https://github.com/ZhuYun97/RoSA}{\texttt{https://github.com/ZhuYun97/RoSA}}
