  Recently, hyperbolic space has risen as a promising alternative for
semi-supervised graph representation learning. Many efforts have been made to
design hyperbolic versions of neural network operations. However, the inspiring
geometric properties of this unique geometry have not been fully explored yet.
The potency of graph models powered by the hyperbolic space is still largely
underestimated. Besides, the rich information carried by abundant unlabelled
samples is also not well utilized. Inspired by the recently active and emerging
self-supervised learning, in this study, we attempt to enhance the
representation power of hyperbolic graph models by drawing upon the advantages
of contrastive learning. More specifically, we put forward a novel Hyperbolic
Graph Contrastive Learning (HGCL) framework which learns node representations
through multiple hyperbolic spaces to implicitly capture the hierarchical
structure shared between different views. Then, we design a hyperbolic position
consistency (HPC) constraint based on hyperbolic distance and the homophily
assumption to make contrastive learning fit into hyperbolic space. Experimental
results on multiple real-world datasets demonstrate the superiority of the
proposed HGCL as it consistently outperforms competing methods by considerable
margins for the node classification task.
