  Unsupervised graph representation learning has emerged as a powerful tool to
address real-world problems and achieves huge success in the graph learning
domain. Graph contrastive learning is one of the unsupervised graph
representation learning methods, which recently attracts attention from
researchers and has achieved state-of-the-art performances on various tasks.
The key to the success of graph contrastive learning is to construct proper
contrasting pairs to acquire the underlying structural semantics of the graph.
However, this key part is not fully explored currently, most of the ways
generating contrasting pairs focus on augmenting or perturbating graph
structures to obtain different views of the input graph. But such strategies
could degrade the performances via adding noise into the graph, which may
narrow down the field of the applications of graph contrastive learning. In
this paper, we propose a novel graph contrastive learning method, namely
\textbf{D}ual \textbf{S}pace \textbf{G}raph \textbf{C}ontrastive (DSGC)
Learning, to conduct graph contrastive learning among views generated in
different spaces including the hyperbolic space and the Euclidean space. Since
both spaces have their own advantages to represent graph data in the embedding
spaces, we hope to utilize graph contrastive learning to bridge the spaces and
leverage advantages from both sides. The comparison experiment results show
that DSGC achieves competitive or better performances among all the datasets.
In addition, we conduct extensive experiments to analyze the impact of
different graph encoders on DSGC, giving insights about how to better leverage
the advantages of contrastive learning between different spaces.
