Temporal heterogeneous information network (temporal HIN) embedding, aiming
to represent various types of nodes of different timestamps into low
dimensional spaces while preserving structural and semantic information, is of
vital importance in diverse real-life tasks. Researchers have made great
efforts on temporal HIN embedding in Euclidean spaces and got some considerable
achievements. However, there is always a fundamental conflict that many
real-world networks show hierarchical property and power-law distribution, and
are not isometric of Euclidean spaces. Recently, representation learning in
hyperbolic spaces has been proved to be valid for data with hierarchical and
power-law structure. Inspired by this character, we propose a hyperbolic
heterogeneous temporal network embedding (H2TNE) model for temporal HINs.
Specifically, we leverage a temporally and heterogeneously double-constrained
random walk strategy to capture the structural and semantic information, and
then calculate the embedding by exploiting hyperbolic distance in proximity
measurement. Experimental results show that our method has superior performance
on temporal link prediction and node classification compared with SOTA models.

提出了一种基于双重随机游走策略和超伽马空间距离的超伽马异构时间网络嵌入模型，能够保留结构和语义信息，对于时间上的链接预测和节点分类取得了优秀的表现。