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.

本文对当前超伽马线图神经网络的技术细节进行了全面的回顾，并将它们统一到一个通用框架中，并总结了每个组件的变体和相关应用，并提出了一些挑战，可能为进一步发展超伽马线空间的图学习成果提供指导。