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，同时保留结构与语义信息，这在多种实际任务中至关重要。研究者已在欧几里得空间中的时序异质信息网络嵌入方面取得显著进展。然而，许多真实网络呈现层次属性与幂律分布，且与欧几里得空间非同构，这一根本矛盾始终存在。最近，双曲空间中的表示学习已被证明对具有层次与幂律结构的数据有效。受此启发，我们提出一种面向时序异质信息网络的超双曲异质时序网络嵌入（H2TNE）模型。具体而言，我们利用时序与异质双重约束的随机游走策略捕获结构与语义信息，并通过近邻测量中的双曲距离计算嵌入。实验结果表明，与现有最优模型相比，本方法在时序链接预测与节点分类任务中表现更优。