Temporal link prediction, aiming to predict future edges between paired nodes
in a dynamic graph, is of vital importance in diverse applications. However,
existing methods are mainly built upon uniform Euclidean space, which has been
found to be conflict with the power-law distributions of real-world graphs and
unable to represent the hierarchical connections between nodes effectively.
With respect to the special data characteristic, hyperbolic geometry offers an
ideal alternative due to its exponential expansion property. In this paper, we
propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits
the fitness between hyperbolic spaces and data distributions for temporal link
prediction. Specifically, we design two key modules to learn the spatial
topological structures and temporal evolutionary information separately. On the
one hand, a hyperbolic diffusion graph convolution (HDGC) module effectively
aggregates information from a wider range of neighbors. On the other hand, the
internal order of causal correlation between historical states is captured by
hyperbolic dilated causal convolution (HDCC) modules. The whole model is built
upon the hyperbolic spaces to preserve the hierarchical structural information
in the entire data flow. To prove the superiority of HGWaveNet, extensive
experiments are conducted on six real-world graph datasets and the results show
a relative improvement by up to 6.67% on AUC for temporal link prediction over
SOTA methods.

本文提出了基于超几何几何的 HGWaveNet 神经网络，该网络有效利用了超几何空间与数据分布的契合性进行时序边预测，通过设计 2 个关键模块，分别学习空间和时间信息，应用于保留了整个数据流中的分层结构信息，实现了对 6 个实际数据集的超几何拟合，将时序预测的 AUC 相对提高了高达 6.67%。