Hyperbolic graph convolutional networks (HGCN) have demonstrated significant
potential in extracting information from hierarchical graphs. However, existing
HGCNs are limited to shallow architectures, due to the expensive hyperbolic
operations and the over-smoothing issue as depth increases. Although in GCNs,
treatments have been applied to alleviate over-smoothing, developing a
hyperbolic therapy presents distinct challenges since operations should be
carefully designed to fit the hyperbolic nature. Addressing the above
challenges, in this work, we propose DeepHGCN, the first deep multi-layer HGCN
architecture with dramatically improved computational efficiency and
substantially alleviated over-smoothing effect. DeepHGCN presents two key
enablers of deep HGCNs: (1) a novel hyperbolic feature transformation layer
that enables fast and accurate linear maps; and (2) Techniques such as
hyperbolic residual connections and regularization for both weights and
features facilitated by an efficient hyperbolic midpoint method. Extensive
experiments demonstrate that DeepHGCN obtains significant improvements in link
prediction and node classification tasks compared to both Euclidean and shallow
hyperbolic GCN variants.

深度超几何图卷积网络（DeepHGCN）通过引入新的超 bolic 特征转换层和技术，极大地提高了计算效率并显著减轻了过平滑效应。与欧几里德和浅超 bolic GCN 变体相比，DeepHGCN 在链接预测和节点分类任务上取得了显著的改进。