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.

翻译：时序链路预测旨在预测动态图中节点对之间的未来边，在各类应用场景中至关重要。然而，现有方法主要基于均匀欧氏空间构建，这与真实世界图的幂律分布特性存在冲突，且无法有效表达节点间的层次连接关系。针对这一特殊数据特征，双曲几何凭借其指数膨胀特性成为理想替代方案。本文提出HGWaveNet——一种新颖的双曲图神经网络，通过充分利用双曲空间与数据分布之间的适配性实现时序链路预测。具体而言，我们设计了两个关键模块分别学习空间拓扑结构与时序演化信息：一方面，双曲扩散图卷积（HDGC）模块能够有效聚合更广邻域内的信息；另一方面，双曲扩张因果卷积（HDCC）模块捕捉历史状态间的因果关联内在顺序。整个模型构建于双曲空间之上，以保留整个数据流中的层次结构信息。为验证HGWaveNet的优越性，我们在六个真实图数据集上进行了广泛实验，结果表明相较于当前最优方法，其时序链路预测的AUC指标最高提升6.67%。