  Graph neural networks generalize conventional neural networks to
graph-structured data and have received widespread attention due to their
impressive representation ability. In spite of the remarkable achievements, the
performance of Euclidean models in graph-related learning is still bounded and
limited by the representation ability of Euclidean geometry, especially for
datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic space
has gained increasing popularity in processing graph data with tree-like
structure and power-law distribution, owing to its exponential growth property.
In this survey, we comprehensively revisit the technical details of the current
hyperbolic graph neural networks, unifying them into a general framework and
summarizing the variants of each component. More importantly, we present
various HGNN-related applications. Last, we also identify several challenges,
which potentially serve as guidelines for further flourishing the achievements
of graph learning in hyperbolic spaces.
