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.

翻译：时序异质信息网络（temporal HIN）嵌入旨在将不同时间戳的各类节点表示到低维空间中，同时保留结构信息和语义信息，这在多种真实场景任务中至关重要。研究者已在欧氏空间的时序HIN嵌入方面做出了大量努力并取得了显著成果。然而，始终存在一个根本性矛盾：许多真实网络具有层次属性和幂律分布特征，与欧氏空间的等距性质不符。近年来，双曲空间中的表示学习已被证明对具有层次和幂律结构的数据有效。受此特性启发，我们提出了一种面向时序HIN的双曲异质时序网络嵌入（H2TNE）模型。具体而言，我们采用一种时间与异质双重约束的随机游走策略来捕获结构和语义信息，然后利用双曲距离进行邻近度量以计算嵌入。实验结果表明，与当前最优模型相比，我们的方法在时序链路预测和节点分类任务上展现出更优越的性能。